Dietary intake, Nutritional status, and Health outcomes among Vegan, Vegetarian and Omnivore families: results from the observational study

Statistical report - data wrangling and exploration

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Authors and affiliations

Marina Heniková^1,2, Anna Ouřadová¹, Eliška Selinger^1,3, Filip Tichanek⁴, Petra Polakovičová⁴, Dana Hrnčířová², Pavel Dlouhý², Martin Světnička⁵, Eva El-Lababidi⁵, Jana Potočková¹, Tilman Kühn⁶, Monika Cahová⁴, Jan Gojda¹

¹ Department of Internal Medicine, Kralovske Vinohrady University Hospital and Third Faculty of Medicine, Charles University, Prague, Czech Republic.
² Department of Hygiene, Third Faculty of Medicine, Charles University, Prague, Czech Republic.
³ National Health Institute, Prague, Czech Republic.
⁴ Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
⁵ Department of Pediatrics, Kralovske Vinohrady University Hospital and Third Faculty of Medicine, Charles University, Prague, Czech Republic.
⁶ Department of Epidemiology, MedUni, Vienna, Austria.

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This is a statistical report of the study currenlty under review in the Communications Medicine journal.

When using this code or data, cite the original publication:

TO BE ADDED

BibTex citation for the original publication:

TO BE ADDED

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Original GitHub repository: https://github.com/filip-tichanek/kompas_clinical

Statistical reports can be found on the reports hub.

Data analysis is described in detail in the statistical methods report.

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1 Introduction

This project is designed to evaluate and compare clinical outcomes across three distinct dietary strategy groups:

• Vegans
• Vegetarians
• Omnivores

The dataset includes both adults and children, with data clustered within families.

1.1 Main Questions

The study addresses the following key questions:

Q1. Do clinical outcomes vary significantly across different diet strategies?

Q2. Beyond diet group, which factors (e.g., sex, age, breastfeeding status for children, or supplementation when applicable) most strongly influence clinical outcomes? How correlated (“clustered”) are these characteristics within the same family?

Q3. Could the clinical characteristics effectively discriminate between different diet groups?

1.2 Statistical Methods

For full methodological details, see this report. In brief:

• Robust linear mixed-effects models (rLME) were used to estimate adjusted differences between diet groups (Q1) and assess the importance of other variables (Q2), including how much clinical characteristics tend to cluster within families. Covariates included age, sex, breastfeeding status for children, and relevant supplementation factors where applicable.

• Elastic net logistic regression was employed to answer Q3, evaluating whether clinical characteristics provide a strong overall signal distinguishing between diet groups, incorporating a predictive perspective.

All analyses were conducted separately for adults and children.

2 Analysis

2.1 Import initiation file

getwd()
## [1] "/home/ticf/GitRepo/ticf/368_MOCA_kompas_clinical"
setwd('/home/ticf/GitRepo/ticf/368_MOCA_kompas_clinical/')
source('r/368_initiation.R')

2.2 Subject characteristics table

2.2.1 All children characteristics table

Comparison of clinical outcomes across diet groups in children

2.2.1.1 Overall

DatChar_child_all <- run(
  dat_child_all_tosum %>% 
    select(-FAM, -ID) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')
              ) %>%
    modify_caption(
      "Supplementary Table 1. Children characteristics according to food preferences, all children"
      ) %>% 
    add_p() %>% 
    add_q(),
  
  path = 'gitignore/run/DatChar_child_all', reuse = TRUE)


DatChar_child_all

Supplementary Table 1. Children characteristics according to food preferences, all children

Characteristic	OM N = 44 1	VG N = 37 1	VN N = 61 1	p-value 2	q-value 3
aAGE	3.04 (1.50, 5.51)	3.85 (1.78, 5.84)	2.17 (1.24, 3.57)	0.016	0.063
SEX				0.9	>0.9
F	24 (55%)	18 (49%)	31 (51%)
M	20 (45%)	19 (51%)	30 (49%)
aBreastFeed_full	6.00 (5.50, 6.00)	6.00 (6.00, 7.00)	6.00 (5.50, 6.00)	0.002	0.013
aBreastFeed_total	10 (8, 14)	18 (12, 21)	15 (9, 19)	0.006	0.030
Unknown	5	2	6
aBreastFeed_full_stopped	42 (95%)	33 (89%)	56 (92%)	0.6	0.7
aBreastFeed_total_stopped	31 (79%)	24 (69%)	33 (60%)	0.14	0.3
Unknown	5	2	6
dev_delay	1 (2.3%)	1 (2.7%)	1 (1.6%)	>0.9	>0.9
aMASS_Perc	54 (30, 77)	59 (29, 67)	38 (14, 67)	0.2	0.3
aHEIGHT_Perc	43 (23, 76)	55 (33, 70)	40 (11, 63)	0.12	0.2
aBMI_PERC	49 (36, 70)	50 (36, 60)	52 (32, 69)	0.8	0.8
aM_per_H_PERC	55 (32, 72)	50 (33, 63)	51 (35, 68)	0.6	0.8
aGLY	4.40 (4.10, 4.62)	4.61 (4.11, 4.76)	4.42 (4.19, 4.60)	0.9	>0.9
Unknown	1	3	6
aTC	3.98 (3.31, 4.38)	3.80 (3.32, 4.32)	3.71 (3.23, 4.02)	0.14	0.3
Unknown	1	3	6
aHDL	1.28 (0.93, 1.51)	1.25 (1.04, 1.39)	1.25 (1.03, 1.38)	>0.9	>0.9
Unknown	1	3	6
aLDL	2.11 (1.74, 2.47)	2.30 (1.85, 2.55)	1.88 (1.58, 2.22)	0.026	0.084
Unknown	1	3	6
aTG	1.03 (0.70, 1.25)	0.71 (0.56, 0.96)	1.01 (0.72, 1.40)	0.016	0.063
Unknown	1	3	6
aCa	2.55 (2.48, 2.66)	2.57 (2.45, 2.64)	2.58 (2.49, 2.65)	0.8	0.8
Unknown	1	3	5
aP	1.70 (1.56, 1.78)	1.61 (1.49, 1.79)	1.67 (1.55, 1.79)	0.5	0.7
Unknown	1	3	5
aMg	0.83 (0.79, 0.88)	0.85 (0.81, 0.91)	0.87 (0.83, 0.92)	0.023	0.076
Unknown	1	3	5
aSe	0.79 (0.71, 0.95)	0.68 (0.60, 0.91)	0.76 (0.53, 0.94)	0.5	0.7
Unknown	1	4	6
aZn	11.70 (10.70, 13.40)	11.30 (10.70, 12.50)	10.60 (9.80, 12.20)	0.085	0.2
Unknown	1	4	6
aFE	12 (8, 18)	13 (6, 17)	13 (9, 19)	0.8	0.8
Unknown	1	3	5
aVKFE	75 (68, 79)	73 (64, 77)	70 (65, 76)	0.12	0.2
Unknown	1	3	6
aFERR	19 (11, 24)	15 (12, 20)	14 (9, 17)	0.039	0.11
Unknown	1	3	5
aTRF	2.96 (2.70, 3.15)	2.89 (2.52, 3.04)	2.75 (2.56, 3.00)	0.10	0.2
Unknown	1	3	5
aSATTRF	16 (12, 23)	18 (10, 27)	19 (11, 26)	0.6	0.7
Unknown	1	3	5
aTRFINDEX	1.37 (1.04, 1.73)	1.38 (1.06, 1.54)	1.40 (1.17, 1.79)	0.3	0.5
Unknown	1	3	5
aSTRF	1.71 (1.44, 1.83)	1.61 (1.39, 1.74)	1.62 (1.44, 1.87)	0.5	0.7
Unknown	1	3	5
aHGB	123 (113, 127)	124 (117, 133)	120 (115, 126)	0.15	0.3
Unknown	3	4	7
aMCV	78.7 (75.1, 80.6)	78.2 (76.7, 80.3)	80.3 (78.4, 82.0)	0.015	0.063
Unknown	3	4	7
aPTH	2.50 (1.70, 3.80)	2.80 (1.90, 3.30)	2.85 (2.20, 3.70)	0.13	0.3
Unknown	1	4	5
aCros	1.22 (0.94, 1.41)	1.24 (1.08, 1.41)	1.24 (1.13, 1.43)	0.5	0.7
Unknown	1	4	6
aP1NP	555 (433, 920)	580 (447, 879)	669 (562, 1,128)	0.019	0.067
Unknown	1	4	6
aUI	169 (140, 248)	136 (85, 242)	99 (67, 160)	0.002	0.013
Unknown	12	6	12
aUREA	4.50 (3.70, 5.60)	4.25 (3.60, 5.10)	3.95 (2.95, 4.65)	0.016	0.063
Unknown	1	3	5
aCREA	27 (21, 33)	27 (21, 34)	23 (19, 29)	0.017	0.063
Unknown	1	3	5
aUA	222 (198, 266)	228 (205, 249)	223 (188, 258)	0.7	0.8
Unknown	1	3	5
aVIT_AKTB12	94 (75, 125)	91 (66, 122)	143 (95, 217)	<0.001	0.002
Unknown	1	4	6
aHCY	8.90 (7.90, 10.40)	8.35 (6.90, 10.40)	7.35 (6.00, 8.80)	<0.001	0.004
Unknown	1	11	11
aMMA	229 (174, 307)	202 (153, 409)	153 (129, 196)	<0.001	<0.001
Unknown	5	4	10
aVIT_D	75 (61, 96)	87 (72, 111)	99 (78, 118)	<0.001	0.007
Unknown	1	3	5
aFOLAT	13.2 (10.0, 16.9)	17.9 (15.9, 19.5)	18.0 (14.4, 22.1)	<0.001	<0.001
Unknown	1	3	6
aIGF1	107 (69, 156)	126 (64, 180)	81 (57, 116)	0.038	0.11
Unknown	2	6	12
aAL_child	9 (20%)	5 (14%)	3 (4.9%)	0.048	0.12
aSUP_VEG1	0 (0%)	4 (11%)	3 (4.9%)	0.065	0.2
aSup_B12	0 (0%)	25 (68%)	46 (75%)	<0.001	<0.001
aSUP_FOL	2 (4.5%)	1 (2.7%)	2 (3.3%)	>0.9	>0.9
aSUP_vitA	0 (0%)	0 (0%)	2 (3.3%)	0.5	0.7
aSUP_vitB1	0 (0%)	2 (5.4%)	3 (4.9%)	0.3	0.5
aSUP_vit.B5	0 (0%)	2 (5.4%)	3 (4.9%)	0.3	0.5
aSUP_D	21 (48%)	33 (89%)	48 (79%)	<0.001	<0.001
aSUP_Mg	0 (0%)	4 (11%)	3 (4.9%)	0.065	0.2
aSUP_Zn	0 (0%)	0 (0%)	5 (8.2%)	0.043	0.12
aSUP_Se	0 (0%)	0 (0%)	3 (4.9%)	0.3	0.4
aSUP_Ca	0 (0%)	2 (5.4%)	3 (4.9%)	0.3	0.5
aSUP_Fe	2 (4.5%)	4 (11%)	4 (6.6%)	0.6	0.7
aSUP_Iod	0 (0%)	3 (8.1%)	6 (9.8%)	0.076	0.2
aSUP_Ѡ3	5 (11%)	13 (35%)	39 (64%)	<0.001	<0.001
aSUP_CHLO	0 (0%)	3 (8.1%)	0 (0%)	0.017	0.063
aSUP_ALG
0	44 (100%)	37 (100%)	61 (100%)
aSUP_GB	0 (0%)	1 (2.7%)	0 (0%)	0.3	0.4
aSUP_FORT	0 (0%)	1 (2.7%)	0 (0%)	0.3	0.4
aSUP_PROB	3 (6.8%)	6 (16%)	0 (0%)	0.003	0.018
aSUP_OTH	14 (32%)	9 (24%)	7 (11%)	0.036	0.11
aUr_Krea	5.1 (2.4, 5.9)	3.9 (2.1, 6.6)	2.9 (1.3, 5.3)	0.051	0.13
Unknown	9	1	8
aUr_Ca	1.15 (0.49, 2.25)	1.14 (0.44, 3.10)	0.65 (0.27, 1.86)	0.2	0.3
Unknown	9	1	8
aCa_per_Krea	0.25 (0.11, 0.50)	0.38 (0.12, 0.81)	0.28 (0.19, 0.61)	0.5	0.6
Unknown	9	1	8
aI_per_Krea	344 (205, 614)	325 (148, 669)	361 (199, 503)	0.9	>0.9
Unknown	12	6	13
aP_per_Krea	4.20 (2.77, 6.10)	3.51 (1.68, 4.49)	2.50 (1.44, 3.92)	<0.001	0.004
Unknown	9	1	8
aBiW	3,310 (3,030, 3,650)	3,370 (3,090, 3,600)	3,500 (3,050, 3,690)	>0.9	>0.9
Unknown	0	0	2
aBREAKS	1 (2.3%)	0 (0%)	4 (6.6%)	0.3	0.4
1 Median (Q1, Q3); n (%)
2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
3 False discovery rate correction for multiple testing

DatChar_child_all_df <- DatChar_child_all$table_body %>%
  as_tibble()

2.2.1.2 OM vs VG

DatChar_child_all_OM_VG <- run(
  dat_child_all_tosum %>%
    select(-FAM, -ID) %>%
    filter(GRP == 'OM' | GRP == 'VG') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    modify_caption("OM vs VG, all children") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
   path = 'gitignore/run/DatChar_child_all_OM_VG', reuse = TRUE)

DatChar_child_all_OM_VG

OM vs VG, all children

Characteristic	OM N = 44 1	VG N = 37 1	p-value 2	q-value 3
aAGE	3.04 (1.50, 5.51)	3.85 (1.78, 5.84)	0.5	0.8
SEX			0.6	0.8
F	24 (55%)	18 (49%)
M	20 (45%)	19 (51%)
aBreastFeed_full	6.00 (5.50, 6.00)	6.00 (6.00, 7.00)	0.002	0.022
aBreastFeed_total	10 (8, 14)	18 (12, 21)	0.001	0.022
Unknown	5	2
aBreastFeed_full_stopped	42 (95%)	33 (89%)	0.4	0.8
aBreastFeed_total_stopped	31 (79%)	24 (69%)	0.3	0.8
Unknown	5	2
dev_delay	1 (2.3%)	1 (2.7%)	>0.9	>0.9
aMASS_Perc	54 (30, 77)	59 (29, 67)	0.7	0.8
aHEIGHT_Perc	43 (23, 76)	55 (33, 70)	0.7	0.9
aBMI_PERC	49 (36, 70)	50 (36, 60)	0.5	0.8
aM_per_H_PERC	55 (32, 72)	50 (33, 63)	0.4	0.8
aGLY	4.40 (4.10, 4.62)	4.61 (4.11, 4.76)	0.6	0.8
Unknown	1	3
aTC	3.98 (3.31, 4.38)	3.80 (3.32, 4.32)	0.5	0.8
Unknown	1	3
aHDL	1.28 (0.93, 1.51)	1.25 (1.04, 1.39)	0.8	>0.9
Unknown	1	3
aLDL	2.11 (1.74, 2.47)	2.30 (1.85, 2.55)	0.5	0.8
Unknown	1	3
aTG	1.03 (0.70, 1.25)	0.71 (0.56, 0.96)	0.018	0.2
Unknown	1	3
aCa	2.55 (2.48, 2.66)	2.57 (2.45, 2.64)	0.8	>0.9
Unknown	1	3
aP	1.70 (1.56, 1.78)	1.61 (1.49, 1.79)	0.3	0.8
Unknown	1	3
aMg	0.83 (0.79, 0.88)	0.85 (0.81, 0.91)	0.2	0.8
Unknown	1	3
aSe	0.79 (0.71, 0.95)	0.68 (0.60, 0.91)	0.3	0.8
Unknown	1	4
aZn	11.70 (10.70, 13.40)	11.30 (10.70, 12.50)	0.5	0.8
Unknown	1	4
aFE	12 (8, 18)	13 (6, 17)	0.9	>0.9
Unknown	1	3
aVKFE	75 (68, 79)	73 (64, 77)	0.4	0.8
Unknown	1	3
aFERR	19 (11, 24)	15 (12, 20)	0.6	0.8
Unknown	1	3
aTRF	2.96 (2.70, 3.15)	2.89 (2.52, 3.04)	0.3	0.8
Unknown	1	3
aSATTRF	16 (12, 23)	18 (10, 27)	0.8	>0.9
Unknown	1	3
aTRFINDEX	1.37 (1.04, 1.73)	1.38 (1.06, 1.54)	>0.9	>0.9
Unknown	1	3
aSTRF	1.71 (1.44, 1.83)	1.61 (1.39, 1.74)	0.3	0.8
Unknown	1	3
aHGB	123 (113, 127)	124 (117, 133)	0.3	0.8
Unknown	3	4
aMCV	78.7 (75.1, 80.6)	78.2 (76.7, 80.3)	>0.9	>0.9
Unknown	3	4
aPTH	2.50 (1.70, 3.80)	2.80 (1.90, 3.30)	>0.9	>0.9
Unknown	1	4
aCros	1.22 (0.94, 1.41)	1.24 (1.08, 1.41)	0.4	0.8
Unknown	1	4
aP1NP	555 (433, 920)	580 (447, 879)	0.8	>0.9
Unknown	1	4
aUI	169 (140, 248)	136 (85, 242)	0.2	0.8
Unknown	12	6
aUREA	4.50 (3.70, 5.60)	4.25 (3.60, 5.10)	0.3	0.8
Unknown	1	3
aCREA	27 (21, 33)	27 (21, 34)	0.8	>0.9
Unknown	1	3
aUA	222 (198, 266)	228 (205, 249)	0.8	>0.9
Unknown	1	3
aVIT_AKTB12	94 (75, 125)	91 (66, 122)	0.6	0.8
Unknown	1	4
aHCY	8.90 (7.90, 10.40)	8.35 (6.90, 10.40)	0.3	0.8
Unknown	1	11
aMMA	229 (174, 307)	202 (153, 409)	0.3	0.8
Unknown	5	4
aVIT_D	75 (61, 96)	87 (72, 111)	0.022	0.2
Unknown	1	3
aFOLAT	13.2 (10.0, 16.9)	17.9 (15.9, 19.5)	<0.001	<0.001
Unknown	1	3
aIGF1	107 (69, 156)	126 (64, 180)	0.5	0.8
Unknown	2	6
aAL_child	9 (20%)	5 (14%)	0.4	0.8
aSUP_VEG1	0 (0%)	4 (11%)	0.040	0.3
aSup_B12	0 (0%)	25 (68%)	<0.001	<0.001
aSUP_FOL	2 (4.5%)	1 (2.7%)	>0.9	>0.9
aSUP_vitA
0	44 (100%)	37 (100%)
aSUP_vitB1	0 (0%)	2 (5.4%)	0.2	0.8
aSUP_vit.B5	0 (0%)	2 (5.4%)	0.2	0.8
aSUP_D	21 (48%)	33 (89%)	<0.001	0.002
aSUP_Mg	0 (0%)	4 (11%)	0.040	0.3
aSUP_Zn
0	44 (100%)	37 (100%)
aSUP_Se
0	44 (100%)	37 (100%)
aSUP_Ca	0 (0%)	2 (5.4%)	0.2	0.8
aSUP_Fe	2 (4.5%)	4 (11%)	0.4	0.8
aSUP_Iod	0 (0%)	3 (8.1%)	0.091	0.5
aSUP_Ѡ3	5 (11%)	13 (35%)	0.010	0.12
aSUP_CHLO	0 (0%)	3 (8.1%)	0.091	0.5
aSUP_ALG
0	44 (100%)	37 (100%)
aSUP_GB	0 (0%)	1 (2.7%)	0.5	0.8
aSUP_FORT	0 (0%)	1 (2.7%)	0.5	0.8
aSUP_PROB	3 (6.8%)	6 (16%)	0.3	0.8
aSUP_OTH	14 (32%)	9 (24%)	0.5	0.8
aUr_Krea	5.1 (2.4, 5.9)	3.9 (2.1, 6.6)	0.5	0.8
Unknown	9	1
aUr_Ca	1.15 (0.49, 2.25)	1.14 (0.44, 3.10)	0.6	0.8
Unknown	9	1
aCa_per_Krea	0.25 (0.11, 0.50)	0.38 (0.12, 0.81)	0.2	0.8
Unknown	9	1
aI_per_Krea	344 (205, 614)	325 (148, 669)	0.6	0.8
Unknown	12	6
aP_per_Krea	4.20 (2.77, 6.10)	3.51 (1.68, 4.49)	0.046	0.3
Unknown	9	1
aBiW	3,310 (3,030, 3,650)	3,370 (3,090, 3,600)	>0.9	>0.9
aBREAKS	1 (2.3%)	0 (0%)	>0.9	>0.9
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum exact test
3 False discovery rate correction for multiple testing

2.2.1.3 OM vs VG

DatChar_child_all_OM_VN <- run(
  dat_child_all_tosum %>%
    select(-FAM, -ID) %>%
    filter(GRP == 'OM' | GRP == 'VN') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    modify_caption("OM vs VN, only children >3 year all included") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
   path = 'gitignore/run/DatChar_child_all_OM_VN', reuse=TRUE)

DatChar_child_all_OM_VN

OM vs VN, only children >3 year all included

Characteristic	OM N = 44 1	VN N = 61 1	p-value 2	q-value 3
aAGE	3.04 (1.50, 5.51)	2.17 (1.24, 3.57)	0.051	0.13
SEX			0.7	0.8
F	24 (55%)	31 (51%)
M	20 (45%)	30 (49%)
aBreastFeed_full	6.00 (5.50, 6.00)	6.00 (5.50, 6.00)	>0.9	>0.9
aBreastFeed_total	10 (8, 14)	15 (9, 19)	0.047	0.13
Unknown	5	6
aBreastFeed_full_stopped	42 (95%)	56 (92%)	0.7	0.8
aBreastFeed_total_stopped	31 (79%)	33 (60%)	0.046	0.13
Unknown	5	6
dev_delay	1 (2.3%)	1 (1.6%)	>0.9	>0.9
aMASS_Perc	54 (30, 77)	38 (14, 67)	0.10	0.2
aHEIGHT_Perc	43 (23, 76)	40 (11, 63)	0.2	0.3
aBMI_PERC	49 (36, 70)	52 (32, 69)	0.5	0.7
aM_per_H_PERC	55 (32, 72)	51 (35, 68)	0.5	0.6
aGLY	4.40 (4.10, 4.62)	4.42 (4.19, 4.60)	0.8	>0.9
Unknown	1	6
aTC	3.98 (3.31, 4.38)	3.71 (3.23, 4.02)	0.048	0.13
Unknown	1	6
aHDL	1.28 (0.93, 1.51)	1.25 (1.03, 1.38)	>0.9	>0.9
Unknown	1	6
aLDL	2.11 (1.74, 2.47)	1.88 (1.58, 2.22)	0.073	0.2
Unknown	1	6
aTG	1.03 (0.70, 1.25)	1.01 (0.72, 1.40)	0.8	>0.9
Unknown	1	6
aCa	2.55 (2.48, 2.66)	2.58 (2.49, 2.65)	0.7	0.8
Unknown	1	5
aP	1.70 (1.56, 1.78)	1.67 (1.55, 1.79)	0.7	0.8
Unknown	1	5
aMg	0.83 (0.79, 0.88)	0.87 (0.83, 0.92)	0.006	0.034
Unknown	1	5
aSe	0.79 (0.71, 0.95)	0.76 (0.53, 0.94)	0.4	0.6
Unknown	1	6
aZn	11.70 (10.70, 13.40)	10.60 (9.80, 12.20)	0.049	0.13
Unknown	1	6
aFE	12 (8, 18)	13 (9, 19)	0.5	0.7
Unknown	1	5
aVKFE	75 (68, 79)	70 (65, 76)	0.037	0.12
Unknown	1	6
aFERR	19 (11, 24)	14 (9, 17)	0.018	0.071
Unknown	1	5
aTRF	2.96 (2.70, 3.15)	2.75 (2.56, 3.00)	0.029	0.10
Unknown	1	5
aSATTRF	16 (12, 23)	19 (11, 26)	0.3	0.5
Unknown	1	5
aTRFINDEX	1.37 (1.04, 1.73)	1.40 (1.17, 1.79)	0.2	0.4
Unknown	1	5
aSTRF	1.71 (1.44, 1.83)	1.62 (1.44, 1.87)	>0.9	>0.9
Unknown	1	5
aHGB	123 (113, 127)	120 (115, 126)	0.4	0.6
Unknown	3	7
aMCV	78.7 (75.1, 80.6)	80.3 (78.4, 82.0)	0.015	0.064
Unknown	3	7
aPTH	2.50 (1.70, 3.80)	2.85 (2.20, 3.70)	0.10	0.2
Unknown	1	5
aCros	1.22 (0.94, 1.41)	1.24 (1.13, 1.43)	0.2	0.4
Unknown	1	6
aP1NP	555 (433, 920)	669 (562, 1,128)	0.010	0.049
Unknown	1	6
aUI	169 (140, 248)	99 (67, 160)	<0.001	0.003
Unknown	12	12
aUREA	4.50 (3.70, 5.60)	3.95 (2.95, 4.65)	0.004	0.025
Unknown	1	5
aCREA	27 (21, 33)	23 (19, 29)	0.024	0.084
Unknown	1	5
aUA	222 (198, 266)	223 (188, 258)	0.5	0.7
Unknown	1	5
aVIT_AKTB12	94 (75, 125)	143 (95, 217)	<0.001	0.002
Unknown	1	6
aHCY	8.90 (7.90, 10.40)	7.35 (6.00, 8.80)	<0.001	0.001
Unknown	1	11
aMMA	229 (174, 307)	153 (129, 196)	<0.001	<0.001
Unknown	5	10
aVIT_D	75 (61, 96)	99 (78, 118)	<0.001	0.002
Unknown	1	5
aFOLAT	13.2 (10.0, 16.9)	18.0 (14.4, 22.1)	<0.001	<0.001
Unknown	1	6
aIGF1	107 (69, 156)	81 (57, 116)	0.081	0.2
Unknown	2	12
aAL_child	9 (20%)	3 (4.9%)	0.014	0.061
aSUP_VEG1	0 (0%)	3 (4.9%)	0.3	0.4
aSup_B12	0 (0%)	46 (75%)	<0.001	<0.001
aSUP_FOL	2 (4.5%)	2 (3.3%)	>0.9	>0.9
aSUP_vitA	0 (0%)	2 (3.3%)	0.5	0.7
aSUP_vitB1	0 (0%)	3 (4.9%)	0.3	0.4
aSUP_vit.B5	0 (0%)	3 (4.9%)	0.3	0.4
aSUP_D	21 (48%)	48 (79%)	<0.001	0.007
aSUP_Mg	0 (0%)	3 (4.9%)	0.3	0.4
aSUP_Zn	0 (0%)	5 (8.2%)	0.073	0.2
aSUP_Se	0 (0%)	3 (4.9%)	0.3	0.4
aSUP_Ca	0 (0%)	3 (4.9%)	0.3	0.4
aSUP_Fe	2 (4.5%)	4 (6.6%)	>0.9	>0.9
aSUP_Iod	0 (0%)	6 (9.8%)	0.039	0.12
aSUP_Ѡ3	5 (11%)	39 (64%)	<0.001	<0.001
aSUP_CHLO
0	44 (100%)	61 (100%)
aSUP_ALG
0	44 (100%)	61 (100%)
aSUP_GB
0	44 (100%)	61 (100%)
aSUP_FORT
0	44 (100%)	61 (100%)
aSUP_PROB	3 (6.8%)	0 (0%)	0.071	0.2
aSUP_OTH	14 (32%)	7 (11%)	0.010	0.049
aUr_Krea	5.1 (2.4, 5.9)	2.9 (1.3, 5.3)	0.024	0.084
Unknown	9	8
aUr_Ca	1.15 (0.49, 2.25)	0.65 (0.27, 1.86)	0.3	0.4
Unknown	9	8
aCa_per_Krea	0.25 (0.11, 0.50)	0.28 (0.19, 0.61)	0.4	0.6
Unknown	9	8
aI_per_Krea	344 (205, 614)	361 (199, 503)	0.7	0.8
Unknown	12	13
aP_per_Krea	4.20 (2.77, 6.10)	2.50 (1.44, 3.92)	<0.001	0.001
Unknown	9	8
aBiW	3,310 (3,030, 3,650)	3,500 (3,050, 3,690)	>0.9	>0.9
Unknown	0	2
aBREAKS	1 (2.3%)	4 (6.6%)	0.4	0.6
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
3 False discovery rate correction for multiple testing

2.2.1.4 VG vs VN

DatChar_child_all_VG_VN <- run(
  dat_child_all_tosum %>%
    select(-FAM, -ID) %>%
    filter(GRP == 'VG' | GRP == 'VN') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    modify_caption("VG vs VN, only children >3 year all included") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
   path = 'gitignore/run/DatChar_child_all_VG_VN', reuse=TRUE)

DatChar_child_all_VG_VN

VG vs VN, only children >3 year all included

Characteristic	VG N = 37 1	VN N = 61 1	p-value 2	q-value 3
aAGE	3.85 (1.78, 5.84)	2.17 (1.24, 3.57)	0.007	0.070
SEX			0.8	>0.9
F	18 (49%)	31 (51%)
M	19 (51%)	30 (49%)
aBreastFeed_full	6.00 (6.00, 7.00)	6.00 (5.50, 6.00)	0.002	0.046
aBreastFeed_total	18 (12, 21)	15 (9, 19)	0.13	0.4
Unknown	2	6
aBreastFeed_full_stopped	33 (89%)	56 (92%)	0.7	>0.9
aBreastFeed_total_stopped	24 (69%)	33 (60%)	0.4	0.6
Unknown	2	6
dev_delay	1 (2.7%)	1 (1.6%)	>0.9	>0.9
aMASS_Perc	59 (29, 67)	38 (14, 67)	0.2	0.5
aHEIGHT_Perc	55 (33, 70)	40 (11, 63)	0.045	0.2
aBMI_PERC	50 (36, 60)	52 (32, 69)	0.9	>0.9
aM_per_H_PERC	50 (33, 63)	51 (35, 68)	0.8	>0.9
aGLY	4.61 (4.11, 4.76)	4.42 (4.19, 4.60)	0.6	0.8
Unknown	3	6
aTC	3.80 (3.32, 4.32)	3.71 (3.23, 4.02)	0.3	0.6
Unknown	3	6
aHDL	1.25 (1.04, 1.39)	1.25 (1.03, 1.38)	>0.9	>0.9
Unknown	3	6
aLDL	2.30 (1.85, 2.55)	1.88 (1.58, 2.22)	0.009	0.081
Unknown	3	6
aTG	0.71 (0.56, 0.96)	1.01 (0.72, 1.40)	0.007	0.070
Unknown	3	6
aCa	2.57 (2.45, 2.64)	2.58 (2.49, 2.65)	0.4	0.7
Unknown	3	5
aP	1.61 (1.49, 1.79)	1.67 (1.55, 1.79)	0.4	0.6
Unknown	3	5
aMg	0.85 (0.81, 0.91)	0.87 (0.83, 0.92)	0.2	0.5
Unknown	3	5
aSe	0.68 (0.60, 0.91)	0.76 (0.53, 0.94)	>0.9	>0.9
Unknown	4	6
aZn	11.30 (10.70, 12.50)	10.60 (9.80, 12.20)	0.11	0.3
Unknown	4	6
aFE	13 (6, 17)	13 (9, 19)	0.6	0.8
Unknown	3	5
aVKFE	73 (64, 77)	70 (65, 76)	0.4	0.6
Unknown	3	6
aFERR	15 (12, 20)	14 (9, 17)	0.081	0.3
Unknown	3	5
aTRF	2.89 (2.52, 3.04)	2.75 (2.56, 3.00)	0.4	0.6
Unknown	3	5
aSATTRF	18 (10, 27)	19 (11, 26)	0.6	0.8
Unknown	3	5
aTRFINDEX	1.38 (1.06, 1.54)	1.40 (1.17, 1.79)	0.2	0.4
Unknown	3	5
aSTRF	1.61 (1.39, 1.74)	1.62 (1.44, 1.87)	0.4	0.6
Unknown	3	5
aHGB	124 (117, 133)	120 (115, 126)	0.050	0.2
Unknown	4	7
aMCV	78.2 (76.7, 80.3)	80.3 (78.4, 82.0)	0.016	0.10
Unknown	4	7
aPTH	2.80 (1.90, 3.30)	2.85 (2.20, 3.70)	0.087	0.3
Unknown	4	5
aCros	1.24 (1.08, 1.41)	1.24 (1.13, 1.43)	>0.9	>0.9
Unknown	4	6
aP1NP	580 (447, 879)	669 (562, 1,128)	0.038	0.2
Unknown	4	6
aUI	136 (85, 242)	99 (67, 160)	0.078	0.3
Unknown	6	12
aUREA	4.25 (3.60, 5.10)	3.95 (2.95, 4.65)	0.13	0.4
Unknown	3	5
aCREA	27 (21, 34)	23 (19, 29)	0.012	0.10
Unknown	3	5
aUA	228 (205, 249)	223 (188, 258)	0.5	0.8
Unknown	3	5
aVIT_AKTB12	91 (66, 122)	143 (95, 217)	<0.001	0.046
Unknown	4	6
aHCY	8.35 (6.90, 10.40)	7.35 (6.00, 8.80)	0.026	0.2
Unknown	11	11
aMMA	202 (153, 409)	153 (129, 196)	0.003	0.046
Unknown	4	10
aVIT_D	87 (72, 111)	99 (78, 118)	0.2	0.5
Unknown	3	5
aFOLAT	17.9 (15.9, 19.5)	18.0 (14.4, 22.1)	0.6	0.8
Unknown	3	6
aIGF1	126 (64, 180)	81 (57, 116)	0.015	0.10
Unknown	6	12
aAL_child	5 (14%)	3 (4.9%)	0.15	0.4
aSUP_VEG1	4 (11%)	3 (4.9%)	0.4	0.6
aSup_B12	25 (68%)	46 (75%)	0.4	0.6
aSUP_FOL	1 (2.7%)	2 (3.3%)	>0.9	>0.9
aSUP_vitA	0 (0%)	2 (3.3%)	0.5	0.8
aSUP_vitB1	2 (5.4%)	3 (4.9%)	>0.9	>0.9
aSUP_vit.B5	2 (5.4%)	3 (4.9%)	>0.9	>0.9
aSUP_D	33 (89%)	48 (79%)	0.2	0.4
aSUP_Mg	4 (11%)	3 (4.9%)	0.4	0.6
aSUP_Zn	0 (0%)	5 (8.2%)	0.2	0.4
aSUP_Se	0 (0%)	3 (4.9%)	0.3	0.6
aSUP_Ca	2 (5.4%)	3 (4.9%)	>0.9	>0.9
aSUP_Fe	4 (11%)	4 (6.6%)	0.5	0.7
aSUP_Iod	3 (8.1%)	6 (9.8%)	>0.9	>0.9
aSUP_Ѡ3	13 (35%)	39 (64%)	0.006	0.070
aSUP_CHLO	3 (8.1%)	0 (0%)	0.051	0.2
aSUP_ALG
0	37 (100%)	61 (100%)
aSUP_GB	1 (2.7%)	0 (0%)	0.4	0.6
aSUP_FORT	1 (2.7%)	0 (0%)	0.4	0.6
aSUP_PROB	6 (16%)	0 (0%)	0.002	0.046
aSUP_OTH	9 (24%)	7 (11%)	0.10	0.3
aUr_Krea	3.85 (2.10, 6.55)	2.90 (1.30, 5.30)	0.094	0.3
Unknown	1	8
aUr_Ca	1.14 (0.44, 3.10)	0.65 (0.27, 1.86)	0.082	0.3
Unknown	1	8
aCa_per_Krea	0.38 (0.12, 0.81)	0.28 (0.19, 0.61)	0.6	0.8
Unknown	1	8
aI_per_Krea	325 (148, 669)	361 (199, 503)	>0.9	>0.9
Unknown	6	13
aP_per_Krea	3.51 (1.68, 4.49)	2.50 (1.44, 3.92)	0.073	0.3
Unknown	1	8
aBiW	3,370 (3,090, 3,600)	3,500 (3,050, 3,690)	0.7	>0.9
Unknown	0	2
aBREAKS	0 (0%)	4 (6.6%)	0.3	0.6
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum exact test
3 False discovery rate correction for multiple testing

2.2.2 Children >3 y.o. characteristics table

2.2.2.1 Overall

DatChar_child_old <- run(
  dat_child_old_tosum %>% 
    select(-FAM, -ID) %>% 
    tbl_summary(
      by='GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
  
      modify_caption("Supplementary Table 2. Children characteristics according to food preferences, with only children >3 year old included") %>% 
    
    add_p() %>% 
    add_q(),
  
  path = 'gitignore/run/DatChar_child_old', reuse=TRUE)

DatChar_child_old

Supplementary Table 2. Children characteristics according to food preferences, with only children >3 year old included

Characteristic	OM N = 22 1	VG N = 22 1	VN N = 21 1	p-value 2	q-value 3
aAGE	5.51 (4.27, 6.30)	5.28 (4.40, 6.89)	4.67 (3.57, 5.78)	0.14	0.3
SEX				0.5	0.7
F	13 (59%)	9 (41%)	10 (48%)
M	9 (41%)	13 (59%)	11 (52%)
aBreastFeed_full	6.00 (5.00, 6.00)	7.00 (6.00, 8.00)	6.00 (6.00, 6.00)	<0.001	0.011
aBreastFeed_total	10 (8, 16)	18 (15, 22)	18 (12, 35)	0.003	0.029
Unknown	3	0	2
aBreastFeed_full_stopped
1	22 (100%)	22 (100%)	21 (100%)
aBreastFeed_total_stopped	19 (100%)	22 (100%)	18 (95%)	0.6	0.8
Unknown	3	0	2
dev_delay	1 (4.5%)	1 (4.5%)	1 (4.8%)	>0.9	>0.9
aMASS_Perc	52 (28, 76)	62 (27, 65)	40 (27, 67)	0.6	0.8
aHEIGHT_Perc	41 (18, 76)	57 (37, 74)	42 (11, 61)	0.4	0.7
aBMI_PERC	46 (34, 63)	46 (31, 58)	43 (21, 68)	0.9	>0.9
aM_per_H_PERC	47 (29, 63)	47 (32, 58)	43 (17, 70)	0.8	>0.9
aGLY	4.28 (3.97, 4.74)	4.26 (3.79, 4.72)	4.33 (4.19, 4.56)	>0.9	>0.9
Unknown	0	1	0
aTC	4.05 (3.60, 4.26)	3.70 (3.19, 4.32)	3.42 (3.20, 3.87)	0.045	0.2
Unknown	0	1	0
aHDL	1.35 (1.03, 1.68)	1.31 (1.10, 1.43)	1.23 (1.12, 1.49)	0.7	0.9
Unknown	0	1	0
aLDL	2.15 (1.83, 2.26)	2.11 (1.79, 2.49)	1.71 (1.56, 2.03)	0.029	0.2
Unknown	0	1	0
aTG	0.89 (0.70, 1.22)	0.62 (0.56, 0.92)	0.93 (0.65, 1.10)	0.2	0.3
Unknown	0	1	0
aCa	2.50 (2.44, 2.55)	2.50 (2.45, 2.58)	2.49 (2.47, 2.52)	>0.9	>0.9
Unknown	0	1	0
aP	1.65 (1.48, 1.77)	1.54 (1.44, 1.63)	1.58 (1.43, 1.67)	0.5	0.7
Unknown	0	1	0
aMg	0.82 (0.79, 0.85)	0.83 (0.80, 0.87)	0.86 (0.81, 0.89)	0.090	0.3
Unknown	0	1	0
aSe	0.83 (0.73, 0.92)	0.68 (0.60, 0.91)	0.77 (0.59, 0.86)	0.4	0.7
Unknown	0	1	0
aZn	11.75 (10.80, 13.10)	11.70 (10.90, 13.30)	10.60 (9.60, 13.10)	0.4	0.7
Unknown	0	1	0
aFE	15 (9, 18)	13 (12, 19)	17 (11, 19)	0.8	>0.9
Unknown	0	1	0
aVKFE	72 (66, 78)	73 (63, 77)	67 (62, 74)	0.2	0.4
Unknown	0	1	0
aFERR	22 (17, 30)	19 (15, 26)	16 (12, 22)	0.10	0.3
Unknown	0	1	0
aTRF	2.86 (2.63, 3.09)	2.88 (2.51, 3.05)	2.66 (2.47, 2.95)	0.2	0.4
Unknown	0	1	0
aSATTRF	20 (15, 25)	20 (14, 27)	25 (17, 30)	0.6	0.8
Unknown	0	1	0
aTRFINDEX	1.13 (0.95, 1.40)	1.28 (1.03, 1.39)	1.20 (1.14, 1.43)	0.3	0.6
Unknown	0	1	0
aSTRF	1.49 (1.36, 1.63)	1.60 (1.40, 1.67)	1.54 (1.36, 1.66)	0.6	0.8
Unknown	0	1	0
aHGB	127 (122, 132)	129 (123, 135)	124 (117, 128)	0.090	0.3
Unknown	0	1	0
aMCV	80.25 (78.40, 82.20)	78.70 (76.90, 80.30)	82.70 (80.10, 84.20)	0.002	0.022
Unknown	0	1	0
aPTH	2.55 (1.80, 3.10)	2.80 (1.90, 3.30)	2.70 (2.30, 3.50)	0.8	0.9
Unknown	0	1	0
aCros	1.24 (0.94, 1.52)	1.24 (1.12, 1.41)	1.25 (1.14, 1.41)	>0.9	>0.9
Unknown	0	1	0
aP1NP	454 (397, 511)	492 (442, 649)	572 (519, 644)	0.013	0.090
Unknown	0	1	0
aUI	173 (140, 256)	126 (85, 182)	110 (75, 160)	0.032	0.2
Unknown	2	4	3
aUREA	4.50 (4.00, 5.40)	4.70 (4.30, 5.80)	4.10 (3.90, 4.80)	0.083	0.3
Unknown	0	1	0
aCREA	33 (28, 37)	31 (27, 36)	30 (27, 33)	0.2	0.4
Unknown	0	1	0
aUA	222 (209, 245)	236 (221, 249)	226 (202, 261)	0.6	0.8
Unknown	0	1	0
aVIT_AKTB12	118 (94, 133)	95 (81, 135)	143 (102, 228)	0.070	0.3
Unknown	0	1	0
aHCY	8.50 (8.00, 10.00)	7.95 (6.90, 10.00)	7.65 (6.30, 8.60)	0.13	0.3
Unknown	0	6	3
aMMA	204 (169, 239)	170 (144, 257)	155 (136, 202)	0.067	0.3
Unknown	0	1	0
aVIT_D	70 (59, 87)	85 (70, 97)	94 (77, 104)	0.035	0.2
Unknown	0	1	0
aFOLAT	11.1 (9.2, 13.8)	17.5 (15.0, 18.8)	17.2 (13.1, 21.0)	<0.001	0.004
Unknown	0	1	0
aIGF1	156 (107, 192)	154 (118, 183)	105 (81, 137)	0.065	0.3
Unknown	1	2	1
aAL_child	4 (18%)	4 (18%)	2 (9.5%)	0.8	0.9
aSUP_VEG1	0 (0%)	4 (18%)	2 (9.5%)	0.11	0.3
aSup_B12	0 (0%)	18 (82%)	16 (76%)	<0.001	<0.001
aSUP_FOL	1 (4.5%)	1 (4.5%)	1 (4.8%)	>0.9	>0.9
aSUP_vitA	0 (0%)	0 (0%)	1 (4.8%)	0.3	0.6
aSUP_vitB1	0 (0%)	2 (9.1%)	2 (9.5%)	0.5	0.7
aSUP_vit.B5	0 (0%)	2 (9.1%)	2 (9.5%)	0.5	0.7
aSUP_D	9 (41%)	19 (86%)	15 (71%)	0.005	0.044
aSUP_Mg	0 (0%)	4 (18%)	3 (14%)	0.11	0.3
aSUP_Zn	0 (0%)	0 (0%)	4 (19%)	0.009	0.068
aSUP_Se	0 (0%)	0 (0%)	2 (9.5%)	0.10	0.3
aSUP_Ca	0 (0%)	2 (9.1%)	2 (9.5%)	0.5	0.7
aSUP_Fe	1 (4.5%)	3 (14%)	2 (9.5%)	0.7	0.9
aSUP_Iod	0 (0%)	3 (14%)	4 (19%)	0.087	0.3
aSUP_Ѡ3	3 (14%)	11 (50%)	16 (76%)	<0.001	0.004
aSUP_CHLO	0 (0%)	3 (14%)	0 (0%)	0.10	0.3
aSUP_ALG
0	22 (100%)	22 (100%)	21 (100%)
aSUP_GB	0 (0%)	1 (4.5%)	0 (0%)	>0.9	>0.9
aSUP_FORT	0 (0%)	1 (4.5%)	0 (0%)	>0.9	>0.9
aSUP_PROB	1 (4.5%)	4 (18%)	0 (0%)	0.12	0.3
aSUP_OTH	9 (41%)	5 (23%)	2 (9.5%)	0.056	0.3
aUr_Krea	5.3 (4.8, 8.5)	5.1 (3.4, 6.9)	4.1 (1.6, 9.0)	0.5	0.7
aUr_Ca	1.41 (0.51, 2.39)	1.76 (0.37, 3.47)	0.61 (0.28, 1.26)	0.2	0.4
aCa_per_Krea	0.26 (0.12, 0.46)	0.33 (0.11, 0.78)	0.21 (0.08, 0.38)	0.3	0.6
aI_per_Krea	259 (189, 440)	169 (136, 366)	339 (107, 404)	0.6	0.8
Unknown	2	4	3
aP_per_Krea	3.31 (2.59, 5.41)	3.36 (1.56, 4.83)	1.66 (1.42, 3.00)	<0.001	0.011
aBiW	3,290 (3,018, 3,600)	3,325 (2,990, 3,680)	3,425 (2,785, 3,720)	>0.9	>0.9
Unknown	0	0	1
aBREAKS	1 (4.5%)	0 (0%)	3 (14%)	0.12	0.3
1 Median (Q1, Q3); n (%)
2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
3 False discovery rate correction for multiple testing

DatChar_child_old_df <- DatChar_child_old$table_body %>%
  as_tibble()

2.2.2.2 OM vs VG

DatChar_child_old_OM_VG <- run(
  dat_child_old_tosum %>%
    select(-FAM, -ID) %>%
    filter(GRP == 'OM' | GRP == 'VG') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    modify_caption("OM vs VG, only children >3 year old included") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
   path = 'gitignore/run/DatChar_child_old_OM_VG', reuse=TRUE)

DatChar_child_old_OM_VG

OM vs VG, only children >3 year old included

Characteristic	OM N = 22 1	VG N = 22 1	p-value 2	q-value 3
aAGE	5.51 (4.27, 6.30)	5.28 (4.40, 6.89)	0.8	>0.9
SEX			0.2	0.8
F	13 (59%)	9 (41%)
M	9 (41%)	13 (59%)
aBreastFeed_full	6.00 (5.00, 6.00)	7.00 (6.00, 8.00)	<0.001	0.002
aBreastFeed_total	10 (8, 16)	18 (15, 22)	0.002	0.030
Unknown	3	0
aBreastFeed_full_stopped
1	22 (100%)	22 (100%)
aBreastFeed_total_stopped
1	19 (100%)	22 (100%)
Unknown	3	0
dev_delay	1 (4.5%)	1 (4.5%)	>0.9	>0.9
aMASS_Perc	52 (28, 76)	62 (27, 65)	0.9	>0.9
aHEIGHT_Perc	41 (18, 76)	57 (37, 74)	0.6	>0.9
aBMI_PERC	46 (34, 63)	46 (31, 58)	0.7	>0.9
aM_per_H_PERC	47 (29, 63)	47 (32, 58)	0.5	>0.9
aGLY	4.28 (3.97, 4.74)	4.26 (3.79, 4.72)	0.8	>0.9
Unknown	0	1
aTC	4.05 (3.60, 4.26)	3.70 (3.19, 4.32)	0.3	0.8
Unknown	0	1
aHDL	1.35 (1.03, 1.68)	1.31 (1.10, 1.43)	0.5	>0.9
Unknown	0	1
aLDL	2.15 (1.83, 2.26)	2.11 (1.79, 2.49)	>0.9	>0.9
Unknown	0	1
aTG	0.89 (0.70, 1.22)	0.62 (0.56, 0.92)	0.093	0.6
Unknown	0	1
aCa	2.50 (2.44, 2.55)	2.50 (2.45, 2.58)	0.8	>0.9
Unknown	0	1
aP	1.65 (1.48, 1.77)	1.54 (1.44, 1.63)	0.3	0.8
Unknown	0	1
aMg	0.82 (0.79, 0.85)	0.83 (0.80, 0.87)	0.3	0.8
Unknown	0	1
aSe	0.83 (0.73, 0.92)	0.68 (0.60, 0.91)	0.2	0.8
Unknown	0	1
aZn	11.75 (10.80, 13.10)	11.70 (10.90, 13.30)	>0.9	>0.9
Unknown	0	1
aFE	15 (9, 18)	13 (12, 19)	0.6	>0.9
Unknown	0	1
aVKFE	72 (66, 78)	73 (63, 77)	0.7	>0.9
Unknown	0	1
aFERR	22 (17, 30)	19 (15, 26)	0.7	>0.9
Unknown	0	1
aTRF	2.86 (2.63, 3.09)	2.88 (2.51, 3.05)	0.6	>0.9
Unknown	0	1
aSATTRF	20 (15, 25)	20 (14, 27)	>0.9	>0.9
Unknown	0	1
aTRFINDEX	1.13 (0.95, 1.40)	1.28 (1.03, 1.39)	0.5	>0.9
Unknown	0	1
aSTRF	1.49 (1.36, 1.63)	1.60 (1.40, 1.67)	0.3	0.8
Unknown	0	1
aHGB	127 (122, 132)	129 (123, 135)	0.4	0.8
Unknown	0	1
aMCV	80.25 (78.40, 82.20)	78.70 (76.90, 80.30)	0.13	0.6
Unknown	0	1
aPTH	2.55 (1.80, 3.10)	2.80 (1.90, 3.30)	>0.9	>0.9
Unknown	0	1
aCros	1.24 (0.94, 1.52)	1.24 (1.12, 1.41)	0.7	>0.9
Unknown	0	1
aP1NP	454 (397, 511)	492 (442, 649)	0.11	0.6
Unknown	0	1
aUI	173 (140, 256)	126 (85, 182)	0.041	0.4
Unknown	2	4
aUREA	4.50 (4.00, 5.40)	4.70 (4.30, 5.80)	0.7	>0.9
Unknown	0	1
aCREA	33 (28, 37)	31 (27, 36)	0.7	>0.9
Unknown	0	1
aUA	222 (209, 245)	236 (221, 249)	0.3	0.8
Unknown	0	1
aVIT_AKTB12	118 (94, 133)	95 (81, 135)	0.8	>0.9
Unknown	0	1
aHCY	8.50 (8.00, 10.00)	7.95 (6.90, 10.00)	0.3	0.8
Unknown	0	6
aMMA	204 (169, 239)	170 (144, 257)	0.4	>0.9
Unknown	0	1
aVIT_D	70 (59, 87)	85 (70, 97)	0.047	0.4
Unknown	0	1
aFOLAT	11.1 (9.2, 13.8)	17.5 (15.0, 18.8)	<0.001	0.001
Unknown	0	1
aIGF1	156 (107, 192)	154 (118, 183)	>0.9	>0.9
Unknown	1	2
aAL_child	4 (18%)	4 (18%)	>0.9	>0.9
aSUP_VEG1	0 (0%)	4 (18%)	0.11	0.6
aSup_B12	0 (0%)	18 (82%)	<0.001	<0.001
aSUP_FOL	1 (4.5%)	1 (4.5%)	>0.9	>0.9
aSUP_vitA
0	22 (100%)	22 (100%)
aSUP_vitB1	0 (0%)	2 (9.1%)	0.5	>0.9
aSUP_vit.B5	0 (0%)	2 (9.1%)	0.5	>0.9
aSUP_D	9 (41%)	19 (86%)	0.002	0.028
aSUP_Mg	0 (0%)	4 (18%)	0.11	0.6
aSUP_Zn
0	22 (100%)	22 (100%)
aSUP_Se
0	22 (100%)	22 (100%)
aSUP_Ca	0 (0%)	2 (9.1%)	0.5	>0.9
aSUP_Fe	1 (4.5%)	3 (14%)	0.6	>0.9
aSUP_Iod	0 (0%)	3 (14%)	0.2	0.8
aSUP_Ѡ3	3 (14%)	11 (50%)	0.010	0.10
aSUP_CHLO	0 (0%)	3 (14%)	0.2	0.8
aSUP_ALG
0	22 (100%)	22 (100%)
aSUP_GB	0 (0%)	1 (4.5%)	>0.9	>0.9
aSUP_FORT	0 (0%)	1 (4.5%)	>0.9	>0.9
aSUP_PROB	1 (4.5%)	4 (18%)	0.3	0.8
aSUP_OTH	9 (41%)	5 (23%)	0.2	0.8
aUr_Krea	5.3 (4.8, 8.5)	5.1 (3.4, 6.9)	0.6	>0.9
aUr_Ca	1.41 (0.51, 2.39)	1.76 (0.37, 3.47)	>0.9	>0.9
aCa_per_Krea	0.26 (0.12, 0.46)	0.33 (0.11, 0.78)	0.5	>0.9
aI_per_Krea	259 (189, 440)	169 (136, 366)	0.3	0.8
Unknown	2	4
aP_per_Krea	3.31 (2.59, 5.41)	3.36 (1.56, 4.83)	0.2	0.8
aBiW	3,290 (3,018, 3,600)	3,325 (2,990, 3,680)	>0.9	>0.9
aBREAKS	1 (4.5%)	0 (0%)	>0.9	>0.9
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum exact test
3 False discovery rate correction for multiple testing

2.2.2.3 OM vs VN

DatChar_child_old_OM_VN <- run(
  dat_child_old_tosum %>%
    select(-FAM, -ID) %>%
    filter(GRP == 'OM' | GRP == 'VN') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    modify_caption("OM vs VN, only children >3 year old included") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
   path = 'gitignore/run/DatChar_child_old_OM_VN', reuse=TRUE)

DatChar_child_old_OM_VN

OM vs VN, only children >3 year old included

Characteristic	OM N = 22 1	VN N = 21 1	p-value 2	q-value 3
aAGE	5.51 (4.27, 6.30)	4.67 (3.57, 5.78)	0.14	0.3
SEX			0.5	0.7
F	13 (59%)	10 (48%)
M	9 (41%)	11 (52%)
aBreastFeed_full	6.00 (5.00, 6.00)	6.00 (6.00, 6.00)	0.089	0.2
aBreastFeed_total	10 (8, 16)	18 (12, 35)	0.004	0.049
Unknown	3	2
aBreastFeed_full_stopped
1	22 (100%)	21 (100%)
aBreastFeed_total_stopped	19 (100%)	18 (95%)	>0.9	>0.9
Unknown	3	2
dev_delay	1 (4.5%)	1 (4.8%)	>0.9	>0.9
aMASS_Perc	52 (28, 76)	40 (27, 67)	0.4	0.6
aHEIGHT_Perc	41 (18, 76)	42 (11, 61)	0.6	0.8
aBMI_PERC	46 (34, 63)	43 (21, 68)	0.7	0.8
aM_per_H_PERC	47 (29, 63)	43 (17, 70)	0.8	0.9
aGLY	4.28 (3.97, 4.74)	4.33 (4.19, 4.56)	0.7	0.8
aTC	4.05 (3.60, 4.26)	3.42 (3.20, 3.87)	0.009	0.085
aHDL	1.35 (1.03, 1.68)	1.23 (1.12, 1.49)	0.5	0.7
aLDL	2.15 (1.83, 2.26)	1.71 (1.56, 2.03)	0.017	0.12
aTG	0.89 (0.70, 1.22)	0.93 (0.65, 1.10)	0.8	0.9
aCa	2.50 (2.44, 2.55)	2.49 (2.47, 2.52)	>0.9	>0.9
aP	1.65 (1.48, 1.77)	1.58 (1.43, 1.67)	0.3	0.5
aMg	0.82 (0.79, 0.85)	0.86 (0.81, 0.89)	0.030	0.2
aSe	0.83 (0.73, 0.92)	0.77 (0.59, 0.86)	0.3	0.5
aZn	11.75 (10.80, 13.10)	10.60 (9.60, 13.10)	0.4	0.6
aFE	15 (9, 18)	17 (11, 19)	0.7	0.8
aVKFE	72 (66, 78)	67 (62, 74)	0.10	0.2
aFERR	22 (17, 30)	16 (12, 22)	0.058	0.2
aTRF	2.86 (2.63, 3.09)	2.66 (2.47, 2.95)	0.087	0.2
aSATTRF	20 (15, 25)	25 (17, 30)	0.3	0.5
aTRFINDEX	1.13 (0.95, 1.40)	1.20 (1.14, 1.43)	0.14	0.3
aSTRF	1.49 (1.36, 1.63)	1.54 (1.36, 1.66)	0.7	0.8
aHGB	127 (122, 132)	124 (117, 128)	0.2	0.4
aMCV	80.25 (78.40, 82.20)	82.70 (80.10, 84.20)	0.032	0.2
aPTH	2.55 (1.80, 3.10)	2.70 (2.30, 3.50)	0.5	0.7
aCros	1.24 (0.94, 1.52)	1.25 (1.14, 1.41)	0.7	0.8
aP1NP	454 (397, 511)	572 (519, 644)	0.003	0.040
aUI	173 (140, 256)	110 (75, 160)	0.019	0.12
Unknown	2	3
aUREA	4.50 (4.00, 5.40)	4.10 (3.90, 4.80)	0.071	0.2
aCREA	33 (28, 37)	30 (27, 33)	0.10	0.2
aUA	222 (209, 245)	226 (202, 261)	>0.9	>0.9
aVIT_AKTB12	118 (94, 133)	143 (102, 228)	0.060	0.2
aHCY	8.50 (8.00, 10.00)	7.65 (6.30, 8.60)	0.043	0.2
Unknown	0	3
aMMA	204 (169, 239)	155 (136, 202)	0.013	0.11
aVIT_D	70 (59, 87)	94 (77, 104)	0.022	0.12
aFOLAT	11.1 (9.2, 13.8)	17.2 (13.1, 21.0)	<0.001	0.016
aIGF1	156 (107, 192)	105 (81, 137)	0.051	0.2
Unknown	1	1
aAL_child	4 (18%)	2 (9.5%)	0.7	0.8
aSUP_VEG1	0 (0%)	2 (9.5%)	0.2	0.4
aSup_B12	0 (0%)	16 (76%)	<0.001	<0.001
aSUP_FOL	1 (4.5%)	1 (4.8%)	>0.9	>0.9
aSUP_vitA	0 (0%)	1 (4.8%)	0.5	0.7
aSUP_vitB1	0 (0%)	2 (9.5%)	0.2	0.4
aSUP_vit.B5	0 (0%)	2 (9.5%)	0.2	0.4
aSUP_D	9 (41%)	15 (71%)	0.044	0.2
aSUP_Mg	0 (0%)	3 (14%)	0.11	0.3
aSUP_Zn	0 (0%)	4 (19%)	0.048	0.2
aSUP_Se	0 (0%)	2 (9.5%)	0.2	0.4
aSUP_Ca	0 (0%)	2 (9.5%)	0.2	0.4
aSUP_Fe	1 (4.5%)	2 (9.5%)	0.6	0.8
aSUP_Iod	0 (0%)	4 (19%)	0.048	0.2
aSUP_Ѡ3	3 (14%)	16 (76%)	<0.001	0.001
aSUP_CHLO
0	22 (100%)	21 (100%)
aSUP_ALG
0	22 (100%)	21 (100%)
aSUP_GB
0	22 (100%)	21 (100%)
aSUP_FORT
0	22 (100%)	21 (100%)
aSUP_PROB	1 (4.5%)	0 (0%)	>0.9	>0.9
aSUP_OTH	9 (41%)	2 (9.5%)	0.018	0.12
aUr_Krea	5.3 (4.8, 8.5)	4.1 (1.6, 9.0)	0.2	0.4
aUr_Ca	1.41 (0.51, 2.39)	0.61 (0.28, 1.26)	0.11	0.3
aCa_per_Krea	0.26 (0.12, 0.46)	0.21 (0.08, 0.38)	0.3	0.4
aI_per_Krea	259 (189, 440)	339 (107, 404)	0.6	0.8
Unknown	2	3
aP_per_Krea	3.31 (2.59, 5.41)	1.66 (1.42, 3.00)	<0.001	0.002
aBiW	3,290 (3,018, 3,600)	3,425 (2,785, 3,720)	>0.9	>0.9
Unknown	0	1
aBREAKS	1 (4.5%)	3 (14%)	0.3	0.5
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum exact test
3 False discovery rate correction for multiple testing

2.2.2.4 VG vs VN

DatChar_child_old_VG_VN <- run(
  dat_child_old_tosum %>%
    select(-FAM, -ID) %>%
    filter(GRP == 'VG' | GRP == 'VN') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    modify_caption("VG vs VN, only children >3 year old included") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
   path = 'gitignore/run/DatChar_child_old_VG_VN', reuse=TRUE)

DatChar_child_old_VG_VN

VG vs VN, only children >3 year old included

Characteristic	VG N = 22 1	VN N = 21 1	p-value 2	q-value 3
aAGE	5.28 (4.40, 6.89)	4.67 (3.57, 5.78)	0.063	0.4
SEX			0.7	>0.9
F	9 (41%)	10 (48%)
M	13 (59%)	11 (52%)
aBreastFeed_full	7.00 (6.00, 8.00)	6.00 (6.00, 6.00)	0.064	0.4
aBreastFeed_total	18 (15, 22)	18 (12, 35)	0.8	>0.9
Unknown	0	2
aBreastFeed_full_stopped
1	22 (100%)	21 (100%)
aBreastFeed_total_stopped	22 (100%)	18 (95%)	0.5	0.9
Unknown	0	2
dev_delay	1 (4.5%)	1 (4.8%)	>0.9	>0.9
aMASS_Perc	62 (27, 65)	40 (27, 67)	0.5	0.9
aHEIGHT_Perc	57 (37, 74)	42 (11, 61)	0.2	0.6
aBMI_PERC	46 (31, 58)	43 (21, 68)	0.9	>0.9
aM_per_H_PERC	47 (32, 58)	43 (17, 70)	0.7	>0.9
aGLY	4.26 (3.79, 4.72)	4.33 (4.19, 4.56)	0.8	>0.9
Unknown	1	0
aTC	3.70 (3.19, 4.32)	3.42 (3.20, 3.87)	0.3	0.7
Unknown	1	0
aHDL	1.31 (1.10, 1.43)	1.23 (1.12, 1.49)	>0.9	>0.9
Unknown	1	0
aLDL	2.11 (1.79, 2.49)	1.71 (1.56, 2.03)	0.029	0.4
Unknown	1	0
aTG	0.62 (0.56, 0.92)	0.93 (0.65, 1.10)	0.10	0.5
Unknown	1	0
aCa	2.50 (2.45, 2.58)	2.49 (2.47, 2.52)	>0.9	>0.9
Unknown	1	0
aP	1.54 (1.44, 1.63)	1.58 (1.43, 1.67)	0.8	>0.9
Unknown	1	0
aMg	0.83 (0.80, 0.87)	0.86 (0.81, 0.89)	0.3	0.7
Unknown	1	0
aSe	0.68 (0.60, 0.91)	0.77 (0.59, 0.86)	0.9	>0.9
Unknown	1	0
aZn	11.70 (10.90, 13.30)	10.60 (9.60, 13.10)	0.2	0.6
Unknown	1	0
aFE	13 (12, 19)	17 (11, 19)	0.5	>0.9
Unknown	1	0
aVKFE	73 (63, 77)	67 (62, 74)	0.2	0.7
Unknown	1	0
aFERR	19 (15, 26)	16 (12, 22)	0.076	0.4
Unknown	1	0
aTRF	2.88 (2.51, 3.05)	2.66 (2.47, 2.95)	0.2	0.7
Unknown	1	0
aSATTRF	20 (14, 27)	25 (17, 30)	0.4	0.9
Unknown	1	0
aTRFINDEX	1.28 (1.03, 1.39)	1.20 (1.14, 1.43)	0.3	0.8
Unknown	1	0
aSTRF	1.60 (1.40, 1.67)	1.54 (1.36, 1.66)	0.5	0.9
Unknown	1	0
aHGB	129 (123, 135)	124 (117, 128)	0.036	0.4
Unknown	1	0
aMCV	78.70 (76.90, 80.30)	82.70 (80.10, 84.20)	<0.001	0.043
Unknown	1	0
aPTH	2.80 (1.90, 3.30)	2.70 (2.30, 3.50)	0.6	>0.9
Unknown	1	0
aCros	1.24 (1.12, 1.41)	1.25 (1.14, 1.41)	>0.9	>0.9
Unknown	1	0
aP1NP	492 (442, 649)	572 (519, 644)	0.2	0.6
Unknown	1	0
aUI	126 (85, 182)	110 (75, 160)	0.6	>0.9
Unknown	4	3
aUREA	4.70 (4.30, 5.80)	4.10 (3.90, 4.80)	0.045	0.4
Unknown	1	0
aCREA	31 (27, 36)	30 (27, 33)	0.2	0.6
Unknown	1	0
aUA	236 (221, 249)	226 (202, 261)	0.5	0.9
Unknown	1	0
aVIT_AKTB12	95 (81, 135)	143 (102, 228)	0.038	0.4
Unknown	1	0
aHCY	7.95 (6.90, 10.00)	7.65 (6.30, 8.60)	0.5	0.9
Unknown	6	3
aMMA	170 (144, 257)	155 (136, 202)	0.3	0.7
Unknown	1	0
aVIT_D	85 (70, 97)	94 (77, 104)	0.4	0.8
Unknown	1	0
aFOLAT	17.5 (15.0, 18.8)	17.2 (13.1, 21.0)	0.8	>0.9
Unknown	1	0
aIGF1	154 (118, 183)	105 (81, 137)	0.039	0.4
Unknown	2	1
aAL_child	4 (18%)	2 (9.5%)	0.7	>0.9
aSUP_VEG1	4 (18%)	2 (9.5%)	0.7	>0.9
aSup_B12	18 (82%)	16 (76%)	0.7	>0.9
aSUP_FOL	1 (4.5%)	1 (4.8%)	>0.9	>0.9
aSUP_vitA	0 (0%)	1 (4.8%)	0.5	0.9
aSUP_vitB1	2 (9.1%)	2 (9.5%)	>0.9	>0.9
aSUP_vit.B5	2 (9.1%)	2 (9.5%)	>0.9	>0.9
aSUP_D	19 (86%)	15 (71%)	0.3	0.7
aSUP_Mg	4 (18%)	3 (14%)	>0.9	>0.9
aSUP_Zn	0 (0%)	4 (19%)	0.048	0.4
aSUP_Se	0 (0%)	2 (9.5%)	0.2	0.7
aSUP_Ca	2 (9.1%)	2 (9.5%)	>0.9	>0.9
aSUP_Fe	3 (14%)	2 (9.5%)	>0.9	>0.9
aSUP_Iod	3 (14%)	4 (19%)	0.7	>0.9
aSUP_Ѡ3	11 (50%)	16 (76%)	0.076	0.4
aSUP_CHLO	3 (14%)	0 (0%)	0.2	0.7
aSUP_ALG
0	22 (100%)	21 (100%)
aSUP_GB	1 (4.5%)	0 (0%)	>0.9	>0.9
aSUP_FORT	1 (4.5%)	0 (0%)	>0.9	>0.9
aSUP_PROB	4 (18%)	0 (0%)	0.11	0.5
aSUP_OTH	5 (23%)	2 (9.5%)	0.4	0.9
aUr_Krea	5.1 (3.4, 6.9)	4.1 (1.6, 9.0)	0.4	0.9
aUr_Ca	1.76 (0.37, 3.47)	0.61 (0.28, 1.26)	0.12	0.5
aCa_per_Krea	0.33 (0.11, 0.78)	0.21 (0.08, 0.38)	0.2	0.6
aI_per_Krea	169 (136, 366)	339 (107, 404)	>0.9	>0.9
Unknown	4	3
aP_per_Krea	3.36 (1.56, 4.83)	1.66 (1.42, 3.00)	0.024	0.4
aBiW	3,325 (2,990, 3,680)	3,425 (2,785, 3,720)	>0.9	>0.9
Unknown	0	1
aBREAKS	0 (0%)	3 (14%)	0.11	0.5
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum exact test
3 False discovery rate correction for multiple testing

2.2.3 Children <3 y.o. characteristics table

2.2.3.1 Overall

DatChar_child_young <- run(
  dat_child_young_tosum %>%
    select(-FAM, -ID) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    
    modify_caption("Supplementary Table 3. Children characteristics according to food preferences, with only children <3 year old included") %>% 
    add_p() %>% 
    add_q(),
  path = 'gitignore/run/DatChar_child_young', reuse=TRUE)

DatChar_child_young

Supplementary Table 3. Children characteristics according to food preferences, with only children <3 year old included

Characteristic	OM N = 22 1	VG N = 15 1	VN N = 40 1	p-value 2	q-value 3
aAGE	1.50 (1.01, 2.26)	1.53 (0.72, 2.12)	1.35 (0.87, 2.12)	0.9	>0.9
SEX				0.8	>0.9
F	11 (50%)	9 (60%)	21 (53%)
M	11 (50%)	6 (40%)	19 (48%)
aBreastFeed_full	6.00 (6.00, 6.00)	6.00 (5.50, 6.00)	6.00 (5.00, 6.00)	0.4	0.8
aBreastFeed_total	10 (9, 14)	18 (9, 21)	12 (8, 17)	0.5	0.8
Unknown	2	2	4
aBreastFeed_full_stopped	20 (91%)	11 (73%)	35 (88%)	0.3	0.8
aBreastFeed_total_stopped	12 (60%)	2 (15%)	15 (42%)	0.040	0.2
Unknown	2	2	4
dev_delay
0	22 (100%)	15 (100%)	40 (100%)
aMASS_Perc	63 (33, 79)	48 (30, 74)	34 (14, 68)	0.2	0.7
aHEIGHT_Perc	43 (33, 75)	51 (28, 68)	38 (14, 64)	0.3	0.8
aBMI_PERC	55 (45, 79)	56 (42, 77)	56 (35, 70)	0.7	>0.9
aM_per_H_PERC	58 (45, 79)	61 (39, 70)	53 (39, 66)	0.5	0.9
aGLY	4.48 (4.29, 4.62)	4.66 (4.31, 4.85)	4.42 (4.21, 4.65)	0.4	0.8
Unknown	1	2	6
aTC	3.97 (3.07, 4.40)	3.89 (3.43, 4.30)	3.90 (3.43, 4.03)	0.8	>0.9
Unknown	1	2	6
aHDL	1.10 (0.84, 1.38)	1.06 (1.00, 1.27)	1.25 (0.94, 1.36)	0.7	>0.9
Unknown	1	2	6
aLDL	2.00 (1.50, 2.79)	2.30 (1.92, 2.62)	1.98 (1.65, 2.24)	0.2	0.7
Unknown	1	2	6
aTG	1.14 (0.93, 1.48)	0.80 (0.67, 1.22)	1.09 (0.77, 1.47)	0.4	0.8
Unknown	1	2	6
aCa	2.65 (2.54, 2.72)	2.64 (2.60, 2.72)	2.63 (2.53, 2.68)	0.8	>0.9
Unknown	1	2	5
aP	1.70 (1.66, 1.80)	1.79 (1.69, 1.83)	1.72 (1.62, 1.83)	0.7	>0.9
Unknown	1	2	5
aMg	0.87 (0.82, 0.90)	0.89 (0.85, 0.97)	0.88 (0.84, 0.92)	0.4	0.8
Unknown	1	2	5
aSe	0.75 (0.68, 0.95)	0.73 (0.63, 0.90)	0.75 (0.53, 0.94)	>0.9	>0.9
Unknown	1	3	6
aZn	11.70 (10.70, 13.40)	10.80 (10.25, 11.40)	10.65 (9.80, 11.80)	0.2	0.7
Unknown	1	3	6
aFE	9.6 (8.3, 14.6)	10.2 (5.4, 12.5)	11.1 (7.6, 17.1)	0.4	0.8
Unknown	1	2	5
aVKFE	75 (72, 79)	73 (69, 75)	71 (66, 77)	0.3	0.8
Unknown	1	2	6
aFERR	15 (9, 21)	11 (9, 14)	12 (9, 16)	0.4	0.8
Unknown	1	2	5
aTRF	2.97 (2.85, 3.15)	2.90 (2.73, 2.99)	2.82 (2.60, 3.04)	0.3	0.8
Unknown	1	2	5
aSATTRF	13 (12, 18)	14 (7, 18)	16 (11, 25)	0.4	0.8
Unknown	1	2	5
aTRFINDEX	1.64 (1.33, 2.06)	1.58 (1.43, 1.79)	1.54 (1.32, 1.96)	>0.9	>0.9
Unknown	1	2	5
aSTRF	1.79 (1.71, 1.94)	1.65 (1.38, 1.88)	1.72 (1.51, 1.99)	0.4	0.8
Unknown	1	2	5
aHGB	116 (112, 123)	117 (108, 122)	119 (115, 121)	0.7	>0.9
Unknown	3	3	7
aMCV	76.8 (70.9, 78.8)	77.6 (75.1, 80.7)	79.7 (76.8, 80.8)	0.021	0.2
Unknown	3	3	7
aPTH	2.50 (1.40, 3.80)	2.60 (1.75, 3.30)	3.20 (2.10, 4.00)	0.2	0.7
Unknown	1	3	5
aCros	1.15 (0.99, 1.28)	1.25 (0.91, 1.46)	1.22 (1.13, 1.43)	0.4	0.8
Unknown	1	3	6
aP1NP	920 (585, 1,201)	986 (843, 1,201)	988 (669, 1,201)	0.8	>0.9
Unknown	1	3	6
aUI	169 (127, 232)	190 (134, 271)	98 (65, 198)	0.036	0.2
Unknown	10	2	9
aUREA	4.30 (3.30, 5.60)	3.20 (2.70, 3.90)	3.60 (2.70, 4.40)	0.10	0.4
Unknown	1	2	5
aCREA	21 (19, 23)	20 (19, 23)	19 (18, 23)	0.4	0.8
Unknown	1	2	5
aUA	222 (191, 267)	223 (175, 241)	218 (184, 255)	0.8	>0.9
Unknown	1	2	5
aVIT_AKTB12	84 (72, 93)	59 (44, 90)	138 (89, 187)	<0.001	0.005
Unknown	1	3	6
aHCY	9.3 (7.4, 12.0)	9.0 (6.9, 12.6)	7.1 (6.0, 8.9)	0.004	0.050
Unknown	1	5	8
aMMA	280 (248, 386)	305 (188, 638)	152 (124, 190)	<0.001	<0.001
Unknown	5	3	10
aVIT_D	82 (67, 98)	98 (81, 129)	104 (81, 119)	0.030	0.2
Unknown	1	2	5
aFOLAT	14.4 (12.7, 17.2)	18.2 (16.9, 21.1)	18.1 (15.6, 22.6)	0.014	0.14
Unknown	1	2	6
aIGF1	70 (48, 107)	63 (50, 84)	64 (45, 89)	>0.9	>0.9
Unknown	1	4	11
aAL_child	5 (23%)	1 (6.7%)	1 (2.5%)	0.025	0.2
aSUP_VEG1	0 (0%)	0 (0%)	1 (2.5%)	>0.9	>0.9
aSup_B12	0 (0%)	7 (47%)	30 (75%)	<0.001	<0.001
aSUP_FOL	1 (4.5%)	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_vitA	0 (0%)	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_vitB1	0 (0%)	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_vit.B5	0 (0%)	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_D	12 (55%)	14 (93%)	33 (83%)	0.015	0.14
aSUP_Mg
0	22 (100%)	15 (100%)	40 (100%)
aSUP_Zn	0 (0%)	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_Se	0 (0%)	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_Ca	0 (0%)	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_Fe	1 (4.5%)	1 (6.7%)	2 (5.0%)	>0.9	>0.9
aSUP_Iod	0 (0%)	0 (0%)	2 (5.0%)	0.7	>0.9
aSUP_Ѡ3	2 (9.1%)	2 (13%)	23 (58%)	<0.001	0.002
aSUP_CHLO
0	22 (100%)	15 (100%)	40 (100%)
aSUP_ALG
0	22 (100%)	15 (100%)	40 (100%)
aSUP_GB
0	22 (100%)	15 (100%)	40 (100%)
aSUP_FORT
0	22 (100%)	15 (100%)	40 (100%)
aSUP_PROB	2 (9.1%)	2 (13%)	0 (0%)	0.062	0.3
aSUP_OTH	5 (23%)	4 (27%)	5 (13%)	0.4	0.8
aUr_Krea	3.20 (2.00, 4.90)	2.75 (1.40, 3.80)	1.80 (1.05, 4.35)	0.5	0.8
Unknown	9	1	8
aUr_Ca	0.63 (0.49, 1.94)	1.07 (0.58, 2.61)	1.02 (0.26, 2.06)	0.7	>0.9
Unknown	9	1	8
aCa_per_Krea	0.12 (0.11, 0.78)	0.49 (0.13, 0.88)	0.41 (0.22, 0.78)	0.5	0.8
Unknown	9	1	8
aI_per_Krea	614 (338, 788)	563 (325, 1,089)	402 (263, 786)	0.4	0.8
Unknown	10	2	10
aP_per_Krea	6.41 (3.10, 7.35)	3.53 (2.17, 4.16)	3.12 (1.66, 4.61)	0.022	0.2
Unknown	9	1	8
aBiW	3,430 (3,080, 3,750)	3,400 (3,200, 3,600)	3,500 (3,050, 3,650)	>0.9	>0.9
Unknown	0	0	1
aBREAKS	0 (0%)	0 (0%)	1 (2.5%)	>0.9	>0.9
1 Median (Q1, Q3); n (%)
2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
3 False discovery rate correction for multiple testing

DatChar_child_young_df <- DatChar_child_young$table_body %>%
  as_tibble()

2.2.3.2 OM vs VG

DatChar_child_young_OM_VG <- run(
  dat_child_young_tosum %>%
    select(-FAM, -ID) %>%
    filter(GRP == 'OM' | GRP == 'VG') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    
    modify_caption("OM vs VG, with only children <3 year old included") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
  path = 'gitignore/run/DatChar_child_young_OM_VG', reuse=TRUE)

DatChar_child_young_OM_VG

OM vs VG, with only children <3 year old included

Characteristic	OM N = 22 1	VG N = 15 1	p-value 2	q-value 3
aAGE	1.50 (1.01, 2.26)	1.53 (0.72, 2.12)	0.8	>0.9
SEX			0.5	>0.9
F	11 (50%)	9 (60%)
M	11 (50%)	6 (40%)
aBreastFeed_full	6.00 (6.00, 6.00)	6.00 (5.50, 6.00)	>0.9	>0.9
aBreastFeed_total	10 (9, 14)	18 (9, 21)	0.3	0.9
Unknown	2	2
aBreastFeed_full_stopped	20 (91%)	11 (73%)	0.2	0.9
aBreastFeed_total_stopped	12 (60%)	2 (15%)	0.011	0.2
Unknown	2	2
dev_delay
0	22 (100%)	15 (100%)
aMASS_Perc	63 (33, 79)	48 (30, 74)	0.6	>0.9
aHEIGHT_Perc	43 (33, 75)	51 (28, 68)	0.9	>0.9
aBMI_PERC	55 (45, 79)	56 (42, 77)	0.9	>0.9
aM_per_H_PERC	58 (45, 79)	61 (39, 70)	0.7	>0.9
aGLY	4.48 (4.29, 4.62)	4.66 (4.31, 4.85)	0.2	0.9
Unknown	1	2
aTC	3.97 (3.07, 4.40)	3.89 (3.43, 4.30)	0.9	>0.9
Unknown	1	2
aHDL	1.10 (0.84, 1.38)	1.06 (1.00, 1.27)	>0.9	>0.9
Unknown	1	2
aLDL	2.00 (1.50, 2.79)	2.30 (1.92, 2.62)	0.4	>0.9
Unknown	1	2
aTG	1.14 (0.93, 1.48)	0.80 (0.67, 1.22)	0.2	0.9
Unknown	1	2
aCa	2.65 (2.54, 2.72)	2.64 (2.60, 2.72)	0.8	>0.9
Unknown	1	2
aP	1.70 (1.66, 1.80)	1.79 (1.69, 1.83)	0.5	>0.9
Unknown	1	2
aMg	0.87 (0.82, 0.90)	0.89 (0.85, 0.97)	0.3	0.9
Unknown	1	2
aSe	0.75 (0.68, 0.95)	0.73 (0.63, 0.90)	0.8	>0.9
Unknown	1	3
aZn	11.70 (10.70, 13.40)	10.80 (10.25, 11.40)	0.13	0.9
Unknown	1	3
aFE	10 (8, 15)	10 (5, 13)	0.7	>0.9
Unknown	1	2
aVKFE	75 (72, 79)	73 (69, 75)	0.3	0.9
Unknown	1	2
aFERR	15 (9, 21)	11 (9, 14)	0.3	0.9
Unknown	1	2
aTRF	2.97 (2.85, 3.15)	2.90 (2.73, 2.99)	0.3	0.9
Unknown	1	2
aSATTRF	13 (12, 18)	14 (7, 18)	0.8	>0.9
Unknown	1	2
aTRFINDEX	1.64 (1.33, 2.06)	1.58 (1.43, 1.79)	>0.9	>0.9
Unknown	1	2
aSTRF	1.79 (1.71, 1.94)	1.65 (1.38, 1.88)	0.2	0.9
Unknown	1	2
aHGB	116 (112, 123)	117 (108, 122)	0.8	>0.9
Unknown	3	3
aMCV	76.8 (70.9, 78.8)	77.6 (75.1, 80.7)	0.2	0.9
Unknown	3	3
aPTH	2.50 (1.40, 3.80)	2.60 (1.75, 3.30)	>0.9	>0.9
Unknown	1	3
aCros	1.15 (0.99, 1.28)	1.25 (0.91, 1.46)	0.7	>0.9
Unknown	1	3
aP1NP	920 (585, 1,201)	986 (843, 1,201)	0.7	>0.9
Unknown	1	3
aUI	169 (127, 232)	190 (134, 271)	0.7	>0.9
Unknown	10	2
aUREA	4.30 (3.30, 5.60)	3.20 (2.70, 3.90)	0.063	0.7
Unknown	1	2
aCREA	21 (19, 23)	20 (19, 23)	0.5	>0.9
Unknown	1	2
aUA	222 (191, 267)	223 (175, 241)	0.6	>0.9
Unknown	1	2
aVIT_AKTB12	84 (72, 93)	59 (44, 90)	0.2	0.9
Unknown	1	3
aHCY	9.30 (7.40, 12.00)	9.00 (6.90, 12.60)	0.9	>0.9
Unknown	1	5
aMMA	280 (248, 386)	305 (188, 638)	0.9	>0.9
Unknown	5	3
aVIT_D	82 (67, 98)	98 (81, 129)	0.11	0.9
Unknown	1	2
aFOLAT	14.4 (12.7, 17.2)	18.2 (16.9, 21.1)	0.017	0.2
Unknown	1	2
aIGF1	70 (48, 107)	63 (50, 84)	0.7	>0.9
Unknown	1	4
aAL_child	5 (23%)	1 (6.7%)	0.4	>0.9
aSUP_VEG1
0	22 (100%)	15 (100%)
aSup_B12	0 (0%)	7 (47%)	<0.001	0.035
aSUP_FOL	1 (4.5%)	0 (0%)	>0.9	>0.9
aSUP_vitA
0	22 (100%)	15 (100%)
aSUP_vitB1
0	22 (100%)	15 (100%)
aSUP_vit.B5
0	22 (100%)	15 (100%)
aSUP_D	12 (55%)	14 (93%)	0.014	0.2
aSUP_Mg
0	22 (100%)	15 (100%)
aSUP_Zn
0	22 (100%)	15 (100%)
aSUP_Se
0	22 (100%)	15 (100%)
aSUP_Ca
0	22 (100%)	15 (100%)
aSUP_Fe	1 (4.5%)	1 (6.7%)	>0.9	>0.9
aSUP_Iod
0	22 (100%)	15 (100%)
aSUP_Ѡ3	2 (9.1%)	2 (13%)	>0.9	>0.9
aSUP_CHLO
0	22 (100%)	15 (100%)
aSUP_ALG
0	22 (100%)	15 (100%)
aSUP_GB
0	22 (100%)	15 (100%)
aSUP_FORT
0	22 (100%)	15 (100%)
aSUP_PROB	2 (9.1%)	2 (13%)	>0.9	>0.9
aSUP_OTH	5 (23%)	4 (27%)	>0.9	>0.9
aUr_Krea	3.20 (2.00, 4.90)	2.75 (1.40, 3.80)	0.7	>0.9
Unknown	9	1
aUr_Ca	0.63 (0.49, 1.94)	1.07 (0.58, 2.61)	0.5	>0.9
Unknown	9	1
aCa_per_Krea	0.12 (0.11, 0.78)	0.49 (0.13, 0.88)	0.2	0.9
Unknown	9	1
aI_per_Krea	614 (338, 788)	563 (325, 1,089)	>0.9	>0.9
Unknown	10	2
aP_per_Krea	6.41 (3.10, 7.35)	3.53 (2.17, 4.16)	0.076	0.7
Unknown	9	1
aBiW	3,430 (3,080, 3,750)	3,400 (3,200, 3,600)	>0.9	>0.9
aBREAKS
0	22 (100%)	15 (100%)
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum exact test
3 False discovery rate correction for multiple testing

2.2.3.3 OM vs VN

DatChar_child_young_OM_VN <- run(
  dat_child_young_tosum %>%
    select(-FAM, -ID) %>%
    filter(GRP == 'OM' | GRP == 'VN') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    
    modify_caption("OM vs VN, with only children <3 year old included") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
  path = 'gitignore/run/DatChar_child_young_OM_VN', reuse=TRUE)

DatChar_child_young_OM_VN

OM vs VN, with only children <3 year old included

Characteristic	OM N = 22 1	VN N = 40 1	p-value 2	q-value 3
aAGE	1.50 (1.01, 2.26)	1.35 (0.87, 2.12)	0.7	>0.9
SEX			0.9	>0.9
F	11 (50%)	21 (53%)
M	11 (50%)	19 (48%)
aBreastFeed_full	6.00 (6.00, 6.00)	6.00 (5.00, 6.00)	0.2	0.6
aBreastFeed_total	10 (9, 14)	12 (8, 17)	0.6	>0.9
Unknown	2	4
aBreastFeed_full_stopped	20 (91%)	35 (88%)	>0.9	>0.9
aBreastFeed_total_stopped	12 (60%)	15 (42%)	0.2	0.5
Unknown	2	4
dev_delay
0	22 (100%)	40 (100%)
aMASS_Perc	63 (33, 79)	34 (14, 68)	0.11	0.5
aHEIGHT_Perc	43 (33, 75)	38 (14, 64)	0.2	0.5
aBMI_PERC	55 (45, 79)	56 (35, 70)	0.4	0.8
aM_per_H_PERC	58 (45, 79)	53 (39, 66)	0.3	0.6
aGLY	4.48 (4.29, 4.62)	4.42 (4.21, 4.65)	>0.9	>0.9
Unknown	1	6
aTC	3.97 (3.07, 4.40)	3.90 (3.43, 4.03)	0.5	0.9
Unknown	1	6
aHDL	1.10 (0.84, 1.38)	1.25 (0.94, 1.36)	0.5	0.9
Unknown	1	6
aLDL	2.00 (1.50, 2.79)	1.98 (1.65, 2.24)	0.6	>0.9
Unknown	1	6
aTG	1.14 (0.93, 1.48)	1.09 (0.77, 1.47)	0.7	>0.9
Unknown	1	6
aCa	2.65 (2.54, 2.72)	2.63 (2.53, 2.68)	0.7	>0.9
Unknown	1	5
aP	1.70 (1.66, 1.80)	1.72 (1.62, 1.83)	0.8	>0.9
Unknown	1	5
aMg	0.87 (0.82, 0.90)	0.88 (0.84, 0.92)	0.3	0.6
Unknown	1	5
aSe	0.75 (0.68, 0.95)	0.75 (0.53, 0.94)	0.9	>0.9
Unknown	1	6
aZn	11.70 (10.70, 13.40)	10.65 (9.80, 11.80)	0.083	0.4
Unknown	1	6
aFE	9.6 (8.3, 14.6)	11.1 (7.6, 17.1)	0.3	0.6
Unknown	1	5
aVKFE	75 (72, 79)	71 (66, 77)	0.12	0.5
Unknown	1	6
aFERR	15 (9, 21)	12 (9, 16)	0.3	0.6
Unknown	1	5
aTRF	2.97 (2.85, 3.15)	2.82 (2.60, 3.04)	0.12	0.5
Unknown	1	5
aSATTRF	13 (12, 18)	16 (11, 25)	0.3	0.6
Unknown	1	5
aTRFINDEX	1.64 (1.33, 2.06)	1.54 (1.32, 1.96)	0.9	>0.9
Unknown	1	5
aSTRF	1.79 (1.71, 1.94)	1.72 (1.51, 1.99)	0.4	0.7
Unknown	1	5
aHGB	116 (112, 123)	119 (115, 121)	0.5	0.9
Unknown	3	7
aMCV	76.8 (70.9, 78.8)	79.7 (76.8, 80.8)	0.005	0.056
Unknown	3	7
aPTH	2.50 (1.40, 3.80)	3.20 (2.10, 4.00)	0.2	0.5
Unknown	1	5
aCros	1.15 (0.99, 1.28)	1.22 (1.13, 1.43)	0.14	0.5
Unknown	1	6
aP1NP	920 (585, 1,201)	988 (669, 1,201)	0.6	>0.9
Unknown	1	6
aUI	169 (127, 232)	98 (65, 198)	0.025	0.14
Unknown	10	9
aUREA	4.30 (3.30, 5.60)	3.60 (2.70, 4.40)	0.092	0.4
Unknown	1	5
aCREA	21 (19, 23)	19 (18, 23)	0.2	0.5
Unknown	1	5
aUA	222 (191, 267)	218 (184, 255)	0.7	>0.9
Unknown	1	5
aVIT_AKTB12	84 (72, 93)	138 (89, 187)	0.001	0.018
Unknown	1	6
aHCY	9.30 (7.40, 12.00)	7.10 (6.00, 8.90)	0.002	0.023
Unknown	1	8
aMMA	280 (248, 386)	152 (124, 190)	<0.001	<0.001
Unknown	5	10
aVIT_D	82 (67, 98)	104 (81, 119)	0.008	0.065
Unknown	1	5
aFOLAT	14.4 (12.7, 17.2)	18.1 (15.6, 22.6)	0.009	0.065
Unknown	1	6
aIGF1	70 (48, 107)	64 (45, 89)	0.8	>0.9
Unknown	1	11
aAL_child	5 (23%)	1 (2.5%)	0.018	0.11
aSUP_VEG1	0 (0%)	1 (2.5%)	>0.9	>0.9
aSup_B12	0 (0%)	30 (75%)	<0.001	<0.001
aSUP_FOL	1 (4.5%)	1 (2.5%)	>0.9	>0.9
aSUP_vitA	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_vitB1	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_vit.B5	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_D	12 (55%)	33 (83%)	0.018	0.11
aSUP_Mg
0	22 (100%)	40 (100%)
aSUP_Zn	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_Se	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_Ca	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_Fe	1 (4.5%)	2 (5.0%)	>0.9	>0.9
aSUP_Iod	0 (0%)	2 (5.0%)	0.5	0.9
aSUP_Ѡ3	2 (9.1%)	23 (58%)	<0.001	0.004
aSUP_CHLO
0	22 (100%)	40 (100%)
aSUP_ALG
0	22 (100%)	40 (100%)
aSUP_GB
0	22 (100%)	40 (100%)
aSUP_FORT
0	22 (100%)	40 (100%)
aSUP_PROB	2 (9.1%)	0 (0%)	0.12	0.5
aSUP_OTH	5 (23%)	5 (13%)	0.3	0.6
aUr_Krea	3.20 (2.00, 4.90)	1.80 (1.05, 4.35)	0.2	0.6
Unknown	9	8
aUr_Ca	0.63 (0.49, 1.94)	1.02 (0.26, 2.06)	0.9	>0.9
Unknown	9	8
aCa_per_Krea	0.12 (0.11, 0.78)	0.41 (0.22, 0.78)	0.3	0.6
Unknown	9	8
aI_per_Krea	614 (338, 788)	402 (263, 786)	0.3	0.6
Unknown	10	10
aP_per_Krea	6.41 (3.10, 7.35)	3.12 (1.66, 4.61)	0.006	0.057
Unknown	9	8
aBiW	3,430 (3,080, 3,750)	3,500 (3,050, 3,650)	0.8	>0.9
Unknown	0	1
aBREAKS	0 (0%)	1 (2.5%)	>0.9	>0.9
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum exact test
3 False discovery rate correction for multiple testing

2.2.3.4 VG vs VN

DatChar_child_young_VG_VN <- run(
  dat_child_young_tosum %>%
    select(-FAM, -ID) %>%
    filter(GRP == 'VG' | GRP == 'VN') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(
      by = 'GRP',
      type = list(
        aBreastFeed_full ~ 'continuous',
        aBreastFeed_total ~ 'continuous')) %>%
    
    modify_caption("VG vs VN, with only children <3 year old included") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
  path = 'gitignore/run/DatChar_child_young_VG_VN', reuse=TRUE)

DatChar_child_young_VG_VN

VG vs VN, with only children <3 year old included

Characteristic	VG N = 15 1	VN N = 40 1	p-value 2	q-value 3
aAGE	1.53 (0.72, 2.12)	1.35 (0.87, 2.12)	0.9	>0.9
SEX			0.6	>0.9
F	9 (60%)	21 (53%)
M	6 (40%)	19 (48%)
aBreastFeed_full	6.00 (5.50, 6.00)	6.00 (5.00, 6.00)	0.4	>0.9
aBreastFeed_total	18 (9, 21)	12 (8, 17)	0.3	>0.9
Unknown	2	4
aBreastFeed_full_stopped	11 (73%)	35 (88%)	0.2	>0.9
aBreastFeed_total_stopped	2 (15%)	15 (42%)	0.11	0.7
Unknown	2	4
dev_delay
0	15 (100%)	40 (100%)
aMASS_Perc	48 (30, 74)	34 (14, 68)	0.3	>0.9
aHEIGHT_Perc	51 (28, 68)	38 (14, 64)	0.3	>0.9
aBMI_PERC	56 (42, 77)	56 (35, 70)	0.6	>0.9
aM_per_H_PERC	61 (39, 70)	53 (39, 66)	0.5	>0.9
aGLY	4.66 (4.31, 4.85)	4.42 (4.21, 4.65)	0.2	>0.9
Unknown	2	6
aTC	3.89 (3.43, 4.30)	3.90 (3.43, 4.03)	0.7	>0.9
Unknown	2	6
aHDL	1.06 (1.00, 1.27)	1.25 (0.94, 1.36)	0.5	>0.9
Unknown	2	6
aLDL	2.30 (1.92, 2.62)	1.98 (1.65, 2.24)	0.043	0.6
Unknown	2	6
aTG	0.80 (0.67, 1.22)	1.09 (0.77, 1.47)	0.2	>0.9
Unknown	2	6
aCa	2.64 (2.60, 2.72)	2.63 (2.53, 2.68)	0.5	>0.9
Unknown	2	5
aP	1.79 (1.69, 1.83)	1.72 (1.62, 1.83)	0.4	>0.9
Unknown	2	5
aMg	0.89 (0.85, 0.97)	0.88 (0.84, 0.92)	0.7	>0.9
Unknown	2	5
aSe	0.73 (0.63, 0.90)	0.75 (0.53, 0.94)	0.9	>0.9
Unknown	3	6
aZn	10.80 (10.25, 11.40)	10.65 (9.80, 11.80)	>0.9	>0.9
Unknown	3	6
aFE	10.2 (5.4, 12.5)	11.1 (7.6, 17.1)	0.3	>0.9
Unknown	2	5
aVKFE	73 (69, 75)	71 (66, 77)	0.8	>0.9
Unknown	2	6
aFERR	11 (9, 14)	12 (9, 16)	0.6	>0.9
Unknown	2	5
aTRF	2.90 (2.73, 2.99)	2.82 (2.60, 3.04)	0.7	>0.9
Unknown	2	5
aSATTRF	14 (7, 18)	16 (11, 25)	0.3	>0.9
Unknown	2	5
aTRFINDEX	1.58 (1.43, 1.79)	1.54 (1.32, 1.96)	>0.9	>0.9
Unknown	2	5
aSTRF	1.65 (1.38, 1.88)	1.72 (1.51, 1.99)	0.4	>0.9
Unknown	2	5
aHGB	117 (108, 122)	119 (115, 121)	0.6	>0.9
Unknown	3	7
aMCV	77.60 (75.10, 80.70)	79.70 (76.80, 80.80)	0.4	>0.9
Unknown	3	7
aPTH	2.60 (1.75, 3.30)	3.20 (2.10, 4.00)	0.11	0.7
Unknown	3	5
aCros	1.25 (0.91, 1.46)	1.22 (1.13, 1.43)	0.7	>0.9
Unknown	3	6
aP1NP	986 (843, 1,201)	988 (669, 1,201)	0.8	>0.9
Unknown	3	6
aUI	190 (134, 271)	98 (65, 198)	0.060	0.6
Unknown	2	9
aUREA	3.20 (2.70, 3.90)	3.60 (2.70, 4.40)	0.4	>0.9
Unknown	2	5
aCREA	20 (19, 23)	19 (18, 23)	0.6	>0.9
Unknown	2	5
aUA	223 (175, 241)	218 (184, 255)	0.7	>0.9
Unknown	2	5
aVIT_AKTB12	59 (44, 90)	138 (89, 187)	0.002	0.057
Unknown	3	6
aHCY	9.0 (6.9, 12.6)	7.1 (6.0, 8.9)	0.043	0.6
Unknown	5	8
aMMA	305 (188, 638)	152 (124, 190)	<0.001	0.015
Unknown	3	10
aVIT_D	98 (81, 129)	104 (81, 119)	>0.9	>0.9
Unknown	2	5
aFOLAT	18.2 (16.9, 21.1)	18.1 (15.6, 22.6)	>0.9	>0.9
Unknown	2	6
aIGF1	63 (50, 84)	64 (45, 89)	>0.9	>0.9
Unknown	4	11
aAL_child	1 (6.7%)	1 (2.5%)	0.5	>0.9
aSUP_VEG1	0 (0%)	1 (2.5%)	>0.9	>0.9
aSup_B12	7 (47%)	30 (75%)	0.059	0.6
aSUP_FOL	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_vitA	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_vitB1	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_vit.B5	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_D	14 (93%)	33 (83%)	0.4	>0.9
aSUP_Mg
0	15 (100%)	40 (100%)
aSUP_Zn	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_Se	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_Ca	0 (0%)	1 (2.5%)	>0.9	>0.9
aSUP_Fe	1 (6.7%)	2 (5.0%)	>0.9	>0.9
aSUP_Iod	0 (0%)	2 (5.0%)	>0.9	>0.9
aSUP_Ѡ3	2 (13%)	23 (58%)	0.003	0.074
aSUP_CHLO
0	15 (100%)	40 (100%)
aSUP_ALG
0	15 (100%)	40 (100%)
aSUP_GB
0	15 (100%)	40 (100%)
aSUP_FORT
0	15 (100%)	40 (100%)
aSUP_PROB	2 (13%)	0 (0%)	0.071	0.6
aSUP_OTH	4 (27%)	5 (13%)	0.2	>0.9
aUr_Krea	2.75 (1.40, 3.80)	1.80 (1.05, 4.35)	0.5	>0.9
Unknown	1	8
aUr_Ca	1.07 (0.58, 2.61)	1.02 (0.26, 2.06)	0.5	>0.9
Unknown	1	8
aCa_per_Krea	0.49 (0.13, 0.88)	0.41 (0.22, 0.78)	0.8	>0.9
Unknown	1	8
aI_per_Krea	563 (325, 1,089)	402 (263, 786)	0.3	>0.9
Unknown	2	10
aP_per_Krea	3.53 (2.17, 4.16)	3.12 (1.66, 4.61)	0.5	>0.9
Unknown	1	8
aBiW	3,400 (3,200, 3,600)	3,500 (3,050, 3,650)	>0.9	>0.9
Unknown	0	1
aBREAKS	0 (0%)	1 (2.5%)	>0.9	>0.9
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum exact test; Pearson’s Chi-squared test; Wilcoxon rank sum test; Fisher’s exact test
3 False discovery rate correction for multiple testing

2.2.4 Adults characteristics table

Comparison of clinical outcomes across diet groups in adults

2.2.4.1 Overall

DatChar_adult <- run(
  dat_adult %>% 
    select(-FAM, -ID) %>% 
    tbl_summary(by = 'GRP') %>%
    modify_caption("Supplementary Table 4. Adults characteristics according to food preferences") %>% 
    add_p() %>% 
    add_q(),
  
  path = 'gitignore/run/DatChar_adult', reuse=TRUE)

DatChar_adult

Supplementary Table 4. Adults characteristics according to food preferences

Characteristic	OM N = 50 1	VG N = 45 1	VN N = 92 1	p-value 2	q-value 3
aAGE	35.8 (32.8, 40.0)	36.7 (33.4, 38.2)	33.9 (31.5, 37.2)	0.024	0.092
SEX				>0.9	>0.9
F	25 (50%)	23 (51%)	47 (51%)
M	25 (50%)	22 (49%)	45 (49%)
aBMI	24.5 (22.4, 26.7)	22.9 (21.5, 26.1)	22.6 (20.8, 25.6)	0.063	0.2
aWHR	0.79 (0.75, 0.85)	0.78 (0.73, 0.85)	0.78 (0.73, 0.82)	0.4	0.6
Unknown	0	0	4
aHEIGHT	1.76 (1.69, 1.80)	1.74 (1.72, 1.80)	1.73 (1.66, 1.81)	0.6	0.7
aMASS	75 (65, 84)	72 (65, 80)	69 (59, 81)	0.14	0.3
aHG	38 (30, 54)	43 (32, 51)	40 (31, 52)	0.9	>0.9
Unknown	1	0	0
aPFAT	21 (18, 29)	23 (16, 29)	21 (15, 26)	0.4	0.5
aFAT	17 (12, 22)	17 (11, 23)	14 (10, 18)	0.078	0.2
aFFM	54 (48, 68)	56 (49, 64)	53 (45, 65)	0.4	0.5
aSBP	125 (116, 140)	121 (111, 134)	121 (112, 134)	0.2	0.4
aDBP	79 (70, 83)	79 (73, 86)	75 (70, 80)	0.014	0.061
aGLY	4.49 (4.25, 4.77)	4.44 (4.19, 4.66)	4.45 (4.13, 4.71)	0.5	0.6
aTC	4.85 (4.30, 5.40)	4.28 (3.78, 5.02)	4.15 (3.65, 4.68)	<0.001	<0.001
aHDL	1.53 (1.23, 1.74)	1.35 (1.20, 1.68)	1.43 (1.24, 1.69)	0.6	0.8
aLDL	2.81 (2.44, 3.34)	2.36 (2.04, 3.10)	2.23 (1.92, 2.77)	<0.001	<0.001
aTG	0.87 (0.57, 1.16)	0.76 (0.65, 1.02)	0.74 (0.57, 1.06)	0.4	0.6
aCa	2.44 (2.38, 2.52)	2.45 (2.41, 2.50)	2.46 (2.42, 2.51)	0.7	0.8
aP	1.12 (1.06, 1.21)	1.12 (0.97, 1.26)	1.08 (1.01, 1.21)	0.5	0.6
aMg	0.79 (0.77, 0.87)	0.81 (0.78, 0.83)	0.81 (0.77, 0.85)	>0.9	>0.9
aSe	1.00 (0.92, 1.17)	0.97 (0.67, 1.18)	0.97 (0.75, 1.18)	0.4	0.5
aZn	14.05 (12.90, 15.60)	13.10 (11.70, 14.10)	12.10 (10.75, 13.80)	<0.001	<0.001
aFE	20 (16, 24)	15 (12, 25)	19 (15, 25)	0.2	0.3
aVKFE	68 (62, 74)	70 (62, 77)	68 (65, 76)	0.6	0.7
aFERR	38 (23, 83)	25 (16, 50)	26 (14, 35)	0.003	0.015
aTRF	2.71 (2.46, 2.94)	2.77 (2.46, 3.07)	2.70 (2.56, 3.02)	0.6	0.8
aSATTRF	28 (24, 36)	22 (17, 39)	29 (21, 36)	0.2	0.3
aTRFINDEX	0.73 (0.61, 0.93)	0.92 (0.64, 1.17)	0.84 (0.70, 1.02)	0.015	0.061
Unknown	0	1	1
aSTRF	1.18 (1.05, 1.32)	1.25 (1.05, 1.47)	1.21 (1.01, 1.34)	0.2	0.4
Unknown	0	1	0
aHGB	147 (136, 154)	144 (134, 154)	145 (135, 156)	0.8	0.8
aMCV	87.6 (86.0, 89.8)	86.7 (85.1, 89.5)	89.7 (87.0, 91.8)	<0.001	0.004
aPTH	2.95 (2.40, 4.00)	3.00 (2.20, 3.40)	3.30 (2.70, 4.10)	0.026	0.10
aCros	0.36 (0.25, 0.56)	0.36 (0.27, 0.48)	0.41 (0.30, 0.59)	0.2	0.4
aP1NP	45 (33, 60)	43 (37, 61)	52 (40, 73)	0.062	0.2
aUI	120 (81, 213)	94 (41, 171)	91 (45, 152)	0.087	0.2
Unknown	8	5	5
aUREA	4.80 (4.00, 5.70)	4.40 (3.80, 5.10)	3.95 (3.20, 4.50)	<0.001	<0.001
aCREA	70 (59, 81)	66 (58, 78)	64 (56, 72)	0.036	0.12
aUA	306 (249, 365)	290 (246, 350)	300 (247, 348)	>0.9	>0.9
aVIT_AKTB12	84 (71, 109)	75 (54, 106)	90 (66, 124)	0.10	0.2
aHCY	15.2 (11.9, 18.1)	15.8 (12.7, 21.5)	14.2 (12.5, 17.1)	0.2	0.4
Unknown	0	10	14
aMMA	194 (158, 234)	236 (161, 305)	170 (134, 215)	<0.001	0.005
aVIT_D	67 (51, 79)	73 (59, 88)	76 (65, 92)	0.001	0.009
aFOLAT	9.2 (7.3, 11.6)	12.9 (10.1, 15.4)	12.6 (9.7, 16.2)	<0.001	<0.001
aAL_adult	17 (34%)	12 (27%)	12 (13%)	0.011	0.053
aSUP_VEG1	0 (0%)	6 (13%)	4 (4.3%)	0.013	0.059
aSup_B12	1 (2.0%)	23 (51%)	76 (83%)	<0.001	<0.001
aSUP_FOL	1 (2.0%)	0 (0%)	6 (6.5%)	0.2	0.4
aSUP_vitA	0 (0%)	0 (0%)	2 (2.2%)	0.7	0.8
aSUP_vitB1	1 (2.0%)	1 (2.2%)	2 (2.2%)	>0.9	>0.9
aSUP_vit.B5	1 (2.0%)	1 (2.2%)	2 (2.2%)	>0.9	>0.9
aSUP_D	17 (34%)	34 (76%)	65 (71%)	<0.001	<0.001
aSUP_Mg	13 (26%)	11 (24%)	12 (13%)	0.10	0.2
aSUP_Zn	6 (12%)	6 (13%)	5 (5.4%)	0.2	0.4
aSUP_Se	0 (0%)	1 (2.2%)	3 (3.3%)	0.7	0.8
aSUP_Ca	0 (0%)	3 (6.7%)	10 (11%)	0.030	0.11
aSUP_Fe	3 (6.0%)	3 (6.7%)	12 (13%)	0.4	0.5
aSUP_Iod	1 (2.0%)	3 (6.7%)	11 (12%)	0.093	0.2
aSUP_Ѡ3	6 (12%)	14 (31%)	47 (51%)	<0.001	<0.001
aSUP_CHLO	0 (0%)	1 (2.2%)	0 (0%)	0.2	0.4
aSUP_ALG	0 (0%)	2 (4.4%)	2 (2.2%)	0.3	0.4
aSUP_GB	0 (0%)	2 (4.4%)	0 (0%)	0.057	0.2
aSUP_FORT	1 (2.0%)	3 (6.7%)	2 (2.2%)	0.4	0.6
aSUP_PROB	3 (6.0%)	2 (4.4%)	1 (1.1%)	0.2	0.4
aSUP_OTH	15 (30%)	14 (31%)	23 (25%)	0.7	0.8
aUr_Krea	6.2 (2.9, 13.8)	5.8 (2.9, 12.2)	4.9 (2.4, 11.0)	0.5	0.6
Unknown	1	0	0
aUr_Ca	1.01 (0.57, 1.52)	0.80 (0.40, 1.53)	0.61 (0.26, 1.30)	0.081	0.2
Unknown	1	0	0
aCa_per_Krea	0.17 (0.10, 0.25)	0.18 (0.10, 0.25)	0.14 (0.10, 0.21)	0.4	0.6
Unknown	1	0	0
aI_per_Krea	181 (111, 328)	109 (66, 243)	144 (67, 254)	0.11	0.3
Unknown	9	5	5
aP_per_Krea	1.71 (1.33, 2.17)	1.46 (0.76, 2.00)	1.15 (0.69, 1.52)	<0.001	<0.001
Unknown	1	0	0
aBREAKS	27 (54%)	23 (51%)	37 (40%)	0.2	0.4
1 Median (Q1, Q3); n (%)
2 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
3 False discovery rate correction for multiple testing

DatChar_adult_df <- DatChar_adult$table_body %>%
  as_tibble()

2.2.4.2 OM vs VG

DatChar_adult_OM_VG <- run(
  dat_adult %>% 
    select(-FAM, -ID) %>% 
    filter(GRP == 'OM' | GRP == 'VG') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(by = 'GRP') %>%
    modify_caption("OM vs VG. Adults characteristics according to food preferences") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
  
  path = 'gitignore/run/DatChar_adult_OM_VG', reuse=TRUE)

DatChar_adult_OM_VG

OM vs VG. Adults characteristics according to food preferences

Characteristic	OM N = 50 1	VG N = 45 1	p-value 2	q-value 3
aAGE	35.8 (32.8, 40.0)	36.7 (33.4, 38.2)	>0.9	>0.9
SEX			>0.9	>0.9
F	25 (50%)	23 (51%)
M	25 (50%)	22 (49%)
aBMI	24.5 (22.4, 26.7)	22.9 (21.5, 26.1)	0.3	0.6
aWHR	0.79 (0.75, 0.85)	0.78 (0.73, 0.85)	>0.9	>0.9
aHEIGHT	1.76 (1.69, 1.80)	1.74 (1.72, 1.80)	0.8	>0.9
aMASS	75 (65, 84)	72 (65, 80)	0.8	>0.9
aHG	38 (30, 54)	43 (32, 51)	0.6	>0.9
Unknown	1	0
aPFAT	21 (18, 29)	23 (16, 29)	>0.9	>0.9
aFAT	17 (12, 22)	17 (11, 23)	>0.9	>0.9
aFFM	54 (48, 68)	56 (49, 64)	0.7	>0.9
aSBP	125 (116, 140)	121 (111, 134)	0.3	0.6
aDBP	79 (70, 83)	79 (73, 86)	0.3	0.7
aGLY	4.49 (4.25, 4.77)	4.44 (4.19, 4.66)	0.4	0.8
aTC	4.85 (4.30, 5.40)	4.28 (3.78, 5.02)	0.005	0.076
aHDL	1.53 (1.23, 1.74)	1.35 (1.20, 1.68)	0.4	0.7
aLDL	2.81 (2.44, 3.34)	2.36 (2.04, 3.10)	0.016	0.14
aTG	0.87 (0.57, 1.16)	0.76 (0.65, 1.02)	0.4	0.8
aCa	2.44 (2.38, 2.52)	2.45 (2.41, 2.50)	0.5	0.8
aP	1.12 (1.06, 1.21)	1.12 (0.97, 1.26)	>0.9	>0.9
aMg	0.79 (0.77, 0.87)	0.81 (0.78, 0.83)	>0.9	>0.9
aSe	1.00 (0.92, 1.17)	0.97 (0.67, 1.18)	0.2	0.6
aZn	14.05 (12.90, 15.60)	13.10 (11.70, 14.10)	0.006	0.076
aFE	20 (16, 24)	15 (12, 25)	0.11	0.4
aVKFE	68 (62, 74)	70 (62, 77)	0.3	0.7
aFERR	38 (23, 83)	25 (16, 50)	0.025	0.2
aTRF	2.71 (2.46, 2.94)	2.77 (2.46, 3.07)	0.4	0.7
aSATTRF	28 (24, 36)	22 (17, 39)	0.076	0.3
aTRFINDEX	0.73 (0.61, 0.93)	0.92 (0.64, 1.17)	0.013	0.13
Unknown	0	1
aSTRF	1.18 (1.05, 1.32)	1.25 (1.05, 1.47)	0.10	0.4
Unknown	0	1
aHGB	147 (136, 154)	144 (134, 154)	0.4	0.8
aMCV	87.6 (86.0, 89.8)	86.7 (85.1, 89.5)	0.13	0.4
aPTH	2.95 (2.40, 4.00)	3.00 (2.20, 3.40)	0.5	0.8
aCros	0.36 (0.25, 0.56)	0.36 (0.27, 0.48)	0.6	>0.9
aP1NP	45 (33, 60)	43 (37, 61)	0.4	0.8
aUI	120 (81, 213)	94 (41, 171)	0.091	0.4
Unknown	8	5
aUREA	4.80 (4.00, 5.70)	4.40 (3.80, 5.10)	0.2	0.5
aCREA	70 (59, 81)	66 (58, 78)	0.2	0.6
aUA	306 (249, 365)	290 (246, 350)	0.7	>0.9
aVIT_AKTB12	84 (71, 109)	75 (54, 106)	0.12	0.4
aHCY	15.2 (11.9, 18.1)	15.8 (12.7, 21.5)	0.4	0.7
Unknown	0	10
aMMA	194 (158, 234)	236 (161, 305)	0.050	0.3
aVIT_D	67 (51, 79)	73 (59, 88)	0.072	0.3
aFOLAT	9.2 (7.3, 11.6)	12.9 (10.1, 15.4)	<0.001	0.012
aAL_adult	17 (34%)	12 (27%)	0.4	0.8
aSUP_VEG1	0 (0%)	6 (13%)	0.009	0.11
aSup_B12	1 (2.0%)	23 (51%)	<0.001	<0.001
aSUP_FOL	1 (2.0%)	0 (0%)	>0.9	>0.9
aSUP_vitA
0	50 (100%)	45 (100%)
aSUP_vitB1	1 (2.0%)	1 (2.2%)	>0.9	>0.9
aSUP_vit.B5	1 (2.0%)	1 (2.2%)	>0.9	>0.9
aSUP_D	17 (34%)	34 (76%)	<0.001	0.002
aSUP_Mg	13 (26%)	11 (24%)	0.9	>0.9
aSUP_Zn	6 (12%)	6 (13%)	0.8	>0.9
aSUP_Se	0 (0%)	1 (2.2%)	0.5	0.8
aSUP_Ca	0 (0%)	3 (6.7%)	0.10	0.4
aSUP_Fe	3 (6.0%)	3 (6.7%)	>0.9	>0.9
aSUP_Iod	1 (2.0%)	3 (6.7%)	0.3	0.7
aSUP_Ѡ3	6 (12%)	14 (31%)	0.023	0.2
aSUP_CHLO	0 (0%)	1 (2.2%)	0.5	0.8
aSUP_ALG	0 (0%)	2 (4.4%)	0.2	0.6
aSUP_GB	0 (0%)	2 (4.4%)	0.2	0.6
aSUP_FORT	1 (2.0%)	3 (6.7%)	0.3	0.7
aSUP_PROB	3 (6.0%)	2 (4.4%)	>0.9	>0.9
aSUP_OTH	15 (30%)	14 (31%)	>0.9	>0.9
aUr_Krea	6.2 (2.9, 13.8)	5.8 (2.9, 12.2)	0.8	>0.9
Unknown	1	0
aUr_Ca	1.01 (0.57, 1.52)	0.80 (0.40, 1.53)	0.5	0.8
Unknown	1	0
aCa_per_Krea	0.17 (0.10, 0.25)	0.18 (0.10, 0.25)	>0.9	>0.9
Unknown	1	0
aI_per_Krea	181 (111, 328)	109 (66, 243)	0.046	0.3
Unknown	9	5
aP_per_Krea	1.71 (1.33, 2.17)	1.46 (0.76, 2.00)	0.074	0.3
Unknown	1	0
aBREAKS	27 (54%)	23 (51%)	0.8	>0.9
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum exact test
3 False discovery rate correction for multiple testing

2.2.4.3 OM vs VN

DatChar_adult_OM_VN <- run(
  dat_adult %>% 
    select(-FAM, -ID) %>% 
    filter(GRP == 'OM' | GRP == 'VN') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(by = 'GRP') %>%
    modify_caption("OM vs VN. Adults characteristics according to food preferences") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
  
  path = 'gitignore/run/DatChar_adult_OM_VN', reuse=TRUE)

DatChar_adult_OM_VN

OM vs VN. Adults characteristics according to food preferences

Characteristic	OM N = 50 1	VN N = 92 1	p-value 2	q-value 3
aAGE	35.8 (32.8, 40.0)	33.9 (31.5, 37.2)	0.035	0.11
SEX			>0.9	>0.9
F	25 (50%)	47 (51%)
M	25 (50%)	45 (49%)
aBMI	24.5 (22.4, 26.7)	22.6 (20.8, 25.6)	0.020	0.080
aWHR	0.79 (0.75, 0.85)	0.78 (0.73, 0.82)	0.2	0.4
Unknown	0	4
aHEIGHT	1.76 (1.69, 1.80)	1.73 (1.66, 1.81)	0.5	0.7
aMASS	75 (65, 84)	69 (59, 81)	0.078	0.2
aHG	38 (30, 54)	40 (31, 52)	0.8	>0.9
Unknown	1	0
aPFAT	21 (18, 29)	21 (15, 26)	0.2	0.4
aFAT	17 (12, 22)	14 (10, 18)	0.039	0.11
aFFM	54 (48, 68)	53 (45, 65)	0.2	0.4
aSBP	125 (116, 140)	121 (112, 134)	0.083	0.2
aDBP	79 (70, 83)	75 (70, 80)	0.11	0.2
aGLY	4.49 (4.25, 4.77)	4.45 (4.13, 4.71)	0.2	0.4
aTC	4.85 (4.30, 5.40)	4.15 (3.65, 4.68)	<0.001	<0.001
aHDL	1.53 (1.23, 1.74)	1.43 (1.24, 1.69)	0.5	0.7
aLDL	2.81 (2.44, 3.34)	2.23 (1.92, 2.77)	<0.001	<0.001
aTG	0.87 (0.57, 1.16)	0.74 (0.57, 1.06)	0.2	0.4
aCa	2.44 (2.38, 2.52)	2.46 (2.42, 2.51)	0.5	0.7
aP	1.12 (1.06, 1.21)	1.08 (1.01, 1.21)	0.2	0.4
aMg	0.79 (0.77, 0.87)	0.81 (0.77, 0.85)	>0.9	>0.9
aSe	1.00 (0.92, 1.17)	0.97 (0.75, 1.18)	0.2	0.4
aZn	14.05 (12.90, 15.60)	12.10 (10.75, 13.80)	<0.001	<0.001
aFE	20 (16, 24)	19 (15, 25)	>0.9	>0.9
aVKFE	68 (62, 74)	68 (65, 76)	0.5	0.6
aFERR	38 (23, 83)	26 (14, 35)	<0.001	0.005
aTRF	2.71 (2.46, 2.94)	2.70 (2.56, 3.02)	0.5	0.6
aSATTRF	28 (24, 36)	29 (21, 36)	0.8	>0.9
aTRFINDEX	0.73 (0.61, 0.93)	0.84 (0.70, 1.02)	0.010	0.047
Unknown	0	1
aSTRF	1.18 (1.05, 1.32)	1.21 (1.01, 1.34)	0.6	0.7
aHGB	147 (136, 154)	145 (135, 156)	0.7	0.8
aMCV	87.6 (86.0, 89.8)	89.7 (87.0, 91.8)	0.019	0.080
aPTH	2.95 (2.40, 4.00)	3.30 (2.70, 4.10)	0.063	0.2
aCros	0.36 (0.25, 0.56)	0.41 (0.30, 0.59)	0.3	0.4
aP1NP	45 (33, 60)	52 (40, 73)	0.026	0.093
aUI	120 (81, 213)	91 (45, 152)	0.033	0.11
Unknown	8	5
aUREA	4.80 (4.00, 5.70)	3.95 (3.20, 4.50)	<0.001	<0.001
aCREA	70 (59, 81)	64 (56, 72)	0.009	0.046
aUA	306 (249, 365)	300 (247, 348)	0.7	0.8
aVIT_AKTB12	84 (71, 109)	90 (66, 124)	0.6	0.7
aHCY	15.2 (11.9, 18.1)	14.2 (12.5, 17.1)	0.4	0.6
Unknown	0	14
aMMA	194 (158, 234)	170 (134, 215)	0.024	0.089
aVIT_D	67 (51, 79)	76 (65, 92)	<0.001	0.003
aFOLAT	9.2 (7.3, 11.6)	12.6 (9.7, 16.2)	<0.001	<0.001
aAL_adult	17 (34%)	12 (13%)	0.003	0.018
aSUP_VEG1	0 (0%)	4 (4.3%)	0.3	0.4
aSup_B12	1 (2.0%)	76 (83%)	<0.001	<0.001
aSUP_FOL	1 (2.0%)	6 (6.5%)	0.4	0.6
aSUP_vitA	0 (0%)	2 (2.2%)	0.5	0.7
aSUP_vitB1	1 (2.0%)	2 (2.2%)	>0.9	>0.9
aSUP_vit.B5	1 (2.0%)	2 (2.2%)	>0.9	>0.9
aSUP_D	17 (34%)	65 (71%)	<0.001	<0.001
aSUP_Mg	13 (26%)	12 (13%)	0.053	0.15
aSUP_Zn	6 (12%)	5 (5.4%)	0.2	0.4
aSUP_Se	0 (0%)	3 (3.3%)	0.6	0.7
aSUP_Ca	0 (0%)	10 (11%)	0.015	0.066
aSUP_Fe	3 (6.0%)	12 (13%)	0.2	0.4
aSUP_Iod	1 (2.0%)	11 (12%)	0.056	0.2
aSUP_Ѡ3	6 (12%)	47 (51%)	<0.001	<0.001
aSUP_CHLO
0	50 (100%)	92 (100%)
aSUP_ALG	0 (0%)	2 (2.2%)	0.5	0.7
aSUP_GB
0	50 (100%)	92 (100%)
aSUP_FORT	1 (2.0%)	2 (2.2%)	>0.9	>0.9
aSUP_PROB	3 (6.0%)	1 (1.1%)	0.13	0.3
aSUP_OTH	15 (30%)	23 (25%)	0.5	0.7
aUr_Krea	6.2 (2.9, 13.8)	4.9 (2.4, 11.0)	0.3	0.4
Unknown	1	0
aUr_Ca	1.01 (0.57, 1.52)	0.61 (0.26, 1.30)	0.035	0.11
Unknown	1	0
aCa_per_Krea	0.17 (0.10, 0.25)	0.14 (0.10, 0.21)	0.3	0.4
Unknown	1	0
aI_per_Krea	181 (111, 328)	144 (67, 254)	0.11	0.2
Unknown	9	5
aP_per_Krea	1.71 (1.33, 2.17)	1.15 (0.69, 1.52)	<0.001	<0.001
Unknown	1	0
aBREAKS	27 (54%)	37 (40%)	0.11	0.3
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
3 False discovery rate correction for multiple testing

2.2.4.4 VG vs VN

DatChar_adult_VG_VN <- run(
  dat_adult %>% 
    select(-FAM, -ID) %>% 
    filter(GRP == 'VG' | GRP == 'VN') %>%
    mutate(GRP = factor(GRP)) %>% 
    tbl_summary(by = 'GRP') %>%
    modify_caption("VG vs VN. Adults characteristics according to food preferences") %>% 
    add_p() %>% 
    add_q() %>% 
    as_gt(),
  
  path = 'gitignore/run/DatChar_adult_VG_VN', reuse=TRUE)

DatChar_adult_VG_VN

VG vs VN. Adults characteristics according to food preferences

Characteristic	VG N = 45 1	VN N = 92 1	p-value 2	q-value 3
aAGE	36.7 (33.4, 38.2)	33.9 (31.5, 37.2)	0.018	0.2
SEX			>0.9	>0.9
F	23 (51%)	47 (51%)
M	22 (49%)	45 (49%)
aBMI	22.9 (21.5, 26.1)	22.6 (20.8, 25.6)	0.3	0.6
aWHR	0.78 (0.73, 0.85)	0.78 (0.73, 0.82)	0.3	0.6
Unknown	0	4
aHEIGHT	1.74 (1.72, 1.80)	1.73 (1.66, 1.81)	0.3	0.6
aMASS	72 (65, 80)	69 (59, 81)	0.14	0.4
aHG	43 (32, 51)	40 (31, 52)	0.7	0.8
aPFAT	23 (16, 29)	21 (15, 26)	0.3	0.6
aFAT	17 (11, 23)	14 (10, 18)	0.11	0.4
aFFM	56 (49, 64)	53 (45, 65)	0.4	0.6
aSBP	121 (111, 134)	121 (112, 134)	0.8	>0.9
aDBP	79 (73, 86)	75 (70, 80)	0.005	0.081
aGLY	4.44 (4.19, 4.66)	4.45 (4.13, 4.71)	>0.9	>0.9
aTC	4.28 (3.78, 5.02)	4.15 (3.65, 4.68)	0.2	0.4
aHDL	1.35 (1.20, 1.68)	1.43 (1.24, 1.69)	0.6	0.8
aLDL	2.36 (2.04, 3.10)	2.23 (1.92, 2.77)	0.11	0.4
aTG	0.76 (0.65, 1.02)	0.74 (0.57, 1.06)	0.5	0.8
aCa	2.45 (2.41, 2.50)	2.46 (2.42, 2.51)	>0.9	>0.9
aP	1.12 (0.97, 1.26)	1.08 (1.01, 1.21)	0.4	0.6
aMg	0.81 (0.78, 0.83)	0.81 (0.77, 0.85)	0.8	>0.9
aSe	0.97 (0.67, 1.18)	0.97 (0.75, 1.18)	>0.9	>0.9
aZn	13.10 (11.70, 14.10)	12.10 (10.75, 13.80)	0.032	0.2
aFE	15 (12, 25)	19 (15, 25)	0.082	0.4
aVKFE	70 (62, 77)	68 (65, 76)	0.8	>0.9
aFERR	25 (16, 50)	26 (14, 35)	0.4	0.6
aTRF	2.77 (2.46, 3.07)	2.70 (2.56, 3.02)	0.8	>0.9
aSATTRF	22 (17, 39)	29 (21, 36)	0.090	0.4
aTRFINDEX	0.92 (0.64, 1.17)	0.84 (0.70, 1.02)	0.6	0.8
Unknown	1	1
aSTRF	1.25 (1.05, 1.47)	1.21 (1.01, 1.34)	0.2	0.4
Unknown	1	0
aHGB	144 (134, 154)	145 (135, 156)	0.7	>0.9
aMCV	86.7 (85.1, 89.5)	89.7 (87.0, 91.8)	<0.001	0.012
aPTH	3.00 (2.20, 3.40)	3.30 (2.70, 4.10)	0.014	0.2
aCros	0.36 (0.27, 0.48)	0.41 (0.30, 0.59)	0.095	0.4
aP1NP	43 (37, 61)	52 (40, 73)	0.2	0.4
aUI	94 (41, 171)	91 (45, 152)	>0.9	>0.9
Unknown	5	5
aUREA	4.40 (3.80, 5.10)	3.95 (3.20, 4.50)	0.007	0.10
aCREA	66 (58, 78)	64 (56, 72)	0.3	0.6
aUA	290 (246, 350)	300 (247, 348)	0.8	>0.9
aVIT_AKTB12	75 (54, 106)	90 (66, 124)	0.036	0.3
aHCY	15.8 (12.7, 21.5)	14.2 (12.5, 17.1)	0.067	0.4
Unknown	10	14
aMMA	236 (161, 305)	170 (134, 215)	<0.001	0.014
aVIT_D	73 (59, 88)	76 (65, 92)	0.12	0.4
aFOLAT	12.9 (10.1, 15.4)	12.6 (9.7, 16.2)	0.8	>0.9
aAL_adult	12 (27%)	12 (13%)	0.049	0.3
aSUP_VEG1	6 (13%)	4 (4.3%)	0.080	0.4
aSup_B12	23 (51%)	76 (83%)	<0.001	0.008
aSUP_FOL	0 (0%)	6 (6.5%)	0.2	0.4
aSUP_vitA	0 (0%)	2 (2.2%)	>0.9	>0.9
aSUP_vitB1	1 (2.2%)	2 (2.2%)	>0.9	>0.9
aSUP_vit.B5	1 (2.2%)	2 (2.2%)	>0.9	>0.9
aSUP_D	34 (76%)	65 (71%)	0.5	0.8
aSUP_Mg	11 (24%)	12 (13%)	0.094	0.4
aSUP_Zn	6 (13%)	5 (5.4%)	0.2	0.4
aSUP_Se	1 (2.2%)	3 (3.3%)	>0.9	>0.9
aSUP_Ca	3 (6.7%)	10 (11%)	0.5	0.8
aSUP_Fe	3 (6.7%)	12 (13%)	0.4	0.6
aSUP_Iod	3 (6.7%)	11 (12%)	0.5	0.8
aSUP_Ѡ3	14 (31%)	47 (51%)	0.027	0.2
aSUP_CHLO	1 (2.2%)	0 (0%)	0.3	0.6
aSUP_ALG	2 (4.4%)	2 (2.2%)	0.6	0.8
aSUP_GB	2 (4.4%)	0 (0%)	0.11	0.4
aSUP_FORT	3 (6.7%)	2 (2.2%)	0.3	0.6
aSUP_PROB	2 (4.4%)	1 (1.1%)	0.3	0.6
aSUP_OTH	14 (31%)	23 (25%)	0.4	0.7
aUr_Krea	5.8 (2.9, 12.2)	4.9 (2.4, 11.0)	0.4	0.6
aUr_Ca	0.80 (0.40, 1.53)	0.61 (0.26, 1.30)	0.2	0.4
aCa_per_Krea	0.18 (0.10, 0.25)	0.14 (0.10, 0.21)	0.3	0.6
aI_per_Krea	109 (66, 243)	144 (67, 254)	0.5	0.7
Unknown	5	5
aP_per_Krea	1.46 (0.76, 2.00)	1.15 (0.69, 1.52)	0.044	0.3
aBREAKS	23 (51%)	37 (40%)	0.2	0.5
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test
3 False discovery rate correction for multiple testing

2.3 Distribution of numerical characteristics

2.3.1 All children histograms

## Reshaping the data to long format
dat_long <- dat_child_all

dat_long <- dat_long %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3)) %>%
  pivot_longer(cols = everything(), names_to = "variable", values_to = "value") %>% 
  mutate(variable = factor(variable, levels = names(dat_long)))

# Creating the histogram matrix
ggplot(dat_long, aes(x = value)) +
  geom_histogram(bins = 20, fill = "skyblue2", color = "grey60") +
  facet_wrap(~ variable, scales = "free") +
  labs(x = "Value", y = "Count", title = "Histogram of Numerical Variables, all children")

2.3.2 Young children histograms

## Reshaping the data to long format
dat_long <- dat_child_young

dat_long <- dat_long %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3)) %>%
  pivot_longer(cols = everything(), names_to = "variable", values_to = "value") %>% 
  mutate(variable = factor(variable, levels = names(dat_long)))

# Creating the histogram matrix
ggplot(dat_long, aes(x = value)) +
  geom_histogram(bins = 20, fill = "skyblue2", color = "grey60") +
  facet_wrap(~ variable, scales = "free") +
  labs(x = "Value", y = "Count", title = "Histogram of Numerical Variables, young children")

2.3.3 Old children histograms

## Reshaping the data to long format
dat_long <- dat_child_old

dat_long <- dat_long %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3)) %>%
  pivot_longer(cols = everything(), names_to = "variable", values_to = "value") %>% 
  mutate(variable = factor(variable, levels = names(dat_long)))

# Creating the histogram matrix
ggplot(dat_long, aes(x = value)) +
  geom_histogram(bins = 20, fill = "skyblue2", color = "grey60") +
  facet_wrap(~ variable, scales = "free") +
  labs(x = "Value", y = "Count", title = "Histogram of Numerical Variables, old children")

2.3.4 Adults histograms

## Reshaping the data to long format
dat_long <- dat_adult

dat_long <- dat_long %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3)) %>%
  pivot_longer(cols = everything(), names_to = "variable", values_to = "value") %>% 
  mutate(variable = factor(variable, levels = names(dat_long)))

# Creating the histogram matrix
ggplot(dat_long, aes(x = value)) +
  geom_histogram(bins = 20, fill = "skyblue2", color = "grey61") +
  facet_wrap(~ variable, scales = "free") +
  labs(x = "Value", y = "Count", title = "Histogram of Numerical Variables, adults ")

2.4 Plot of group characteristics

2.4.1 All children

## define color
colo <- c('#F9FFAF', '#ACE1A3', '#329243')

## prepare data
df <- dat_child_all

tr <- DatChar_child_all_df %>% 
  filter(!is.na(q.value)) %>% 
  select(variable, q.value)

df <- df %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3) | GRP) 

## define outcomes of interest
outcomes <- data.frame(
  variable = setdiff(names(df), c(
    'GRP', 
    'log2_age', 
    'aBreastFeed_full_duration')))
  
outcomes <- outcomes %>% 
  inner_join(tr, by = 'variable')
  
## define comparison
my_comparisons <- list( c('OM', 'VG'), c('OM', 'VN'), c('VG', 'VN') )

## function to boxplot
boxplot_cond <- function(variable, q.value) {
  
  clean_data <- df %>%
    select(GRP, all_of(variable)) %>%
    drop_na()
  
  upper_limit <- max(clean_data[[variable]], na.rm = TRUE) * 1.5
  
  p <- ggboxplot(clean_data, 
                 x = 'GRP', 
                 y = variable, 
                 fill = 'GRP', 
                 tip.length = 0.15,
                 palette = colo,
                 outlier.shape = 1,
                 lwd = 0.25,
                 outlier.size = 0.8) +
    
    theme(
      plot.title = element_text(size = 10),   # Smaller title
      axis.title = element_text(size = 8),    # Smaller axis titles
      axis.text = element_text(size = 7)      # Smaller axis labels
    ) +
    
    rremove("legend") +
    
    coord_cartesian(ylim = c(0, upper_limit))
  
  if (q.value < 0.05) {
    p <- p + stat_compare_means(
      method = 'wilcox.test',
      label = 'P',
      comparisons = my_comparisons,
      size = 3,
      label.y = upper_limit * c(0.7, 0.8, 0.9))
  }
  
  return(p)
}

# Plot all outcomes
plots <- pmap(outcomes, boxplot_cond)

# Arrange plots in a matrix
plots_arranged <- ggarrange(plotlist = plots, ncol = 8, nrow = 5)
plots_arranged
## $`1`

## 
## $`2`

## 
## attr(,"class")
## [1] "list"      "ggarrange"

2.4.2 Young children

## data
df <- dat_child_young

df <- df %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3) | GRP) 

## defining outcomes of interest
tr <- DatChar_child_young_df %>% 
  filter(!is.na(q.value)) %>% 
  select(variable, q.value)

df <- df %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3) | GRP) 

## define outcomes of interest
outcomes <- data.frame(
  variable = setdiff(names(df), c(
    'GRP', 
    'log2_age', 
    'aBreastFeed_full_duration')))
  
outcomes <- outcomes %>% 
  inner_join(tr, by = 'variable')

## plot all outcomes
plots <- pmap(outcomes, boxplot_cond)

## matrix of plot
plots_arranged <- ggarrange(plotlist = plots, ncol = 9, nrow = 5)
plots_arranged

2.4.3 Old children

## data
df <- dat_child_old

df <- df %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3) | GRP) 

## defining outcomes of interest
tr <- DatChar_child_old_df %>% 
  filter(!is.na(q.value)) %>% 
  select(variable, q.value)

df <- df %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3) | GRP) 

## define outcomes of interest
outcomes <- data.frame(
  variable = setdiff(names(df), c(
    'GRP', 
    'log2_age', 
    'aBreastFeed_full_duration')))
  
outcomes <- outcomes %>% 
  inner_join(tr, by = 'variable')

## plot all outcomes
plots <- pmap(outcomes, boxplot_cond)

## matrix of plot
plots_arranged <- ggarrange(plotlist = plots, ncol = 9, nrow = 5)
plots_arranged

2.4.4 Adults

## data
df <- dat_adult

df <- df %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3) | GRP) %>% 
  select(-aCros)

## defining outcomes of interest
tr <- DatChar_adult_df %>% 
  filter(!is.na(q.value)) %>% 
  select(variable, q.value)

df <- df %>% 
  select(where(~is.numeric(.)  && length(unique(.)) >3) | GRP) 

## define outcomes of interest
outcomes <- data.frame(
  variable = setdiff(names(df), c(
    'GRP', 
    'log2_age')))

outcomes <- outcomes %>% 
  inner_join(tr, by = 'variable')

## plot all outcomes
plots <- pmap(outcomes, boxplot_cond)

## matrix of plot
plots_arranged <- ggarrange(plotlist = plots, ncol = 8, nrow = 6)
plots_arranged

3 Food intake summary

dat_food %>% summary()
##  GRP      Calories.kcal.   Carbohydrates.g.     Fat.g.        Proteins.g.     
##  OM: 71   Min.   : 488.5   Min.   : 49.6    Min.   : 12.94   Min.   :  8.967  
##  VG: 61   1st Qu.:1175.6   1st Qu.:142.9    1st Qu.: 39.76   1st Qu.: 37.557  
##  VN:135   Median :1738.4   Median :200.8    Median : 59.48   Median : 59.423  
##           Mean   :1790.5   Mean   :210.1    Mean   : 64.12   Mean   : 63.414  
##           3rd Qu.:2310.7   3rd Qu.:268.1    3rd Qu.: 82.02   3rd Qu.: 83.641  
##           Max.   :4219.1   Max.   :566.7    Max.   :178.78   Max.   :177.875  
##                                                                               
##     Fiber.g.       Sugars.g.      Saturated.fats..SAFA..g. Cholesterol.mg.   
##  Min.   : 0.00   Min.   : 10.07   Min.   : 0.03763         Min.   :  0.0000  
##  1st Qu.:14.71   1st Qu.: 45.94   1st Qu.: 8.62974         1st Qu.:  0.0684  
##  Median :23.73   Median : 60.14   Median :14.02014         Median : 30.8760  
##  Mean   :27.13   Mean   : 66.07   Mean   :17.20864         Mean   : 64.0970  
##  3rd Qu.:35.63   3rd Qu.: 79.80   3rd Qu.:24.52420         3rd Qu.:102.1615  
##  Max.   :95.39   Max.   :188.59   Max.   :60.13940         Max.   :432.7600  
##                                   NA's   :7                                  
##  Phosphorus.mg.   Magnesium.mg.        Zinc.mg.        Selenium.μg.  
##  Min.   : 103.3   Min.   :  20.67   Min.   : 0.7287   Min.   : 1.38  
##  1st Qu.: 529.1   1st Qu.: 150.26   1st Qu.: 3.9645   1st Qu.:10.68  
##  Median : 816.2   Median : 263.16   Median : 6.6042   Median :19.15  
##  Mean   : 915.8   Mean   : 300.69   Mean   : 8.3542   Mean   :23.06  
##  3rd Qu.:1272.5   3rd Qu.: 401.73   3rd Qu.:10.6941   3rd Qu.:31.20  
##  Max.   :3210.3   Max.   :1031.60   Max.   :45.5507   Max.   :91.06  
##                                                                      
##     Iron.mg.        Calcium.mg.       Iodine.μg.          Age_category
##  Min.   : 0.4823   Min.   :  66.8   Min.   :  4.152   Adult     :154  
##  1st Qu.: 4.5585   1st Qu.: 375.3   1st Qu.: 36.167   Ch < 3 yrs: 65  
##  Median : 8.2290   Median : 588.6   Median : 53.672   Ch > 3 yrs: 48  
##  Mean   : 9.2992   Mean   : 664.2   Mean   : 57.135                   
##  3rd Qu.:12.0101   3rd Qu.: 874.1   3rd Qu.: 68.933                   
##  Max.   :33.1056   Max.   :2095.8   Max.   :323.739                   
## 

3.1 Summary tables

3.1.1 Children all

food_int_child_all <- run(
  dat_food %>%
    filter(Age_category != 'Adult') %>% 
    select(-Age_category) %>% 
    tbl_summary(
      by = 'GRP') %>%
    modify_caption("Nutrients intake in children (all)") %>% 
    add_p() %>% 
    add_q(),
  
  path = 'gitignore/run/food_int_child_all', reuse = TRUE)


food_int_child_all

Nutrients intake in children (all)

Characteristic	OM N = 33 1	VG N = 27 1	VN N = 53 1	p-value 2	q-value 3
Calories.kcal.	1,047 (831, 1,386)	1,108 (967, 1,449)	1,083 (906, 1,370)	0.7	0.8
Carbohydrates.g.	121 (107, 158)	142 (102, 188)	135 (101, 185)	0.4	0.6
Fat.g.	38 (33, 51)	44 (34, 51)	38 (31, 48)	0.6	0.7
Proteins.g.	38 (29, 51)	36 (28, 49)	32 (22, 42)	0.028	0.084
Fiber.g.	12 (7, 15)	16 (11, 22)	16 (9, 26)	0.011	0.042
Sugars.g.	59 (46, 78)	48 (39, 67)	58 (45, 70)	0.3	0.5
Saturated.fats..SAFA..g.	15 (9, 20)	10 (6, 19)	5 (4, 8)	<0.001	<0.001
Unknown	1	1	5
Cholesterol.mg.	101 (64, 130)	78 (23, 98)	7 (0, 83)	<0.001	<0.001
Phosphorus.mg.	598 (359, 751)	518 (296, 762)	396 (238, 672)	0.10	0.2
Magnesium.mg.	136 (85, 169)	144 (72, 204)	141 (91, 239)	0.6	0.7
Zinc.mg.	3.98 (2.74, 5.38)	3.47 (2.59, 5.35)	4.40 (2.72, 6.71)	0.6	0.7
Selenium.μg.	17 (11, 22)	11 (8, 22)	9 (8, 12)	<0.001	0.005
Iron.mg.	3.77 (2.20, 5.61)	4.37 (1.69, 5.69)	4.52 (2.79, 6.99)	0.8	0.8
Calcium.mg.	417 (296, 598)	427 (279, 546)	338 (271, 402)	0.2	0.3
Iodine.μg.	47 (26, 57)	46 (24, 56)	37 (20, 52)	0.3	0.5
1 Median (Q1, Q3)
2 Kruskal-Wallis rank sum test
3 False discovery rate correction for multiple testing

food_int_child_all_df <- food_int_child_all$table_body %>%
  as_tibble()

3.1.2 Children young

food_int_child_young <- run(
  dat_food %>%
    filter(Age_category == 'Ch < 3 yrs') %>% 
    select(-Age_category) %>% 
    tbl_summary(
      by = 'GRP') %>%
    modify_caption("Nutrients intake in children < 3 years of age") %>% 
    add_p() %>% 
    add_q(),
  
  path = 'gitignore/run/food_int_child_young', reuse = TRUE)


food_int_child_young

Nutrients intake in children < 3 years of age

Characteristic	OM N = 16 1	VG N = 12 1	VN N = 37 1	p-value 2	q-value 3
Calories.kcal.	856 (750, 1,027)	980 (780, 1,088)	1,008 (665, 1,349)	0.5	0.7
Carbohydrates.g.	104 (89, 116)	119 (82, 149)	122 (85, 168)	0.3	0.7
Fat.g.	36 (22, 40)	37 (32, 47)	38 (30, 48)	0.4	0.7
Proteins.g.	29 (21, 36)	29 (19, 35)	30 (16, 37)	0.9	0.9
Fiber.g.	8 (4, 12)	12 (5, 17)	15 (8, 19)	0.074	0.3
Sugars.g.	51 (43, 64)	58 (44, 78)	60 (49, 75)	0.5	0.7
Saturated.fats..SAFA..g.	9.5 (5.8, 15.3)	5.2 (2.2, 8.9)	5.2 (3.1, 8.0)	0.032	0.3
Unknown	1	1	5
Cholesterol.mg.	101 (52, 121)	91 (42, 120)	52 (0, 103)	0.037	0.3
Phosphorus.mg.	345 (298, 573)	313 (188, 523)	357 (209, 584)	0.7	0.8
Magnesium.mg.	90 (58, 146)	90 (53, 147)	124 (76, 191)	0.5	0.7
Zinc.mg.	2.90 (2.34, 4.34)	2.86 (2.43, 4.89)	4.36 (2.64, 5.84)	0.2	0.5
Selenium.μg.	12.0 (9.7, 17.1)	8.4 (7.6, 18.8)	8.9 (7.7, 11.2)	0.057	0.3
Iron.mg.	2.33 (1.35, 3.68)	2.64 (1.30, 4.65)	3.69 (1.81, 5.42)	0.3	0.7
Calcium.mg.	337 (290, 541)	286 (271, 388)	337 (271, 401)	0.6	0.7
Iodine.μg.	50 (27, 58)	50 (31, 53)	42 (29, 52)	0.6	0.7
1 Median (Q1, Q3)
2 Kruskal-Wallis rank sum test
3 False discovery rate correction for multiple testing

food_int_child_young_df <- food_int_child_young$table_body %>%
  as_tibble()

3.1.3 Children old

food_int_child_old <- run(
  dat_food %>%
    filter(Age_category == 'Ch > 3 yrs') %>% 
    select(-Age_category) %>% 
    tbl_summary(
      by = 'GRP') %>%
    modify_caption("Nutrients intake in children of 3+ years of age") %>% 
    add_p() %>% 
    add_q(),
  
  path = 'gitignore/run/food_int_child_old', reuse = TRUE)


food_int_child_old

Nutrients intake in children of 3+ years of age

Characteristic	OM N = 17 1	VG N = 15 1	VN N = 16 1	p-value 2	q-value 3
Calories.kcal.	1,386 (1,132, 1,690)	1,369 (1,108, 1,657)	1,250 (1,080, 1,425)	0.6	0.6
Carbohydrates.g.	158 (135, 179)	166 (123, 222)	172 (140, 196)	0.6	0.6
Fat.g.	51 (36, 66)	47 (38, 53)	39 (36, 42)	0.4	0.5
Proteins.g.	50 (46, 60)	42 (35, 59)	42 (29, 45)	0.041	0.12
Fiber.g.	14 (14, 16)	19 (12, 23)	26 (17, 31)	0.003	0.016
Sugars.g.	71 (50, 89)	45 (38, 61)	54 (45, 66)	0.049	0.12
Saturated.fats..SAFA..g.	20 (16, 26)	14 (11, 21)	7 (5, 10)	<0.001	<0.001
Cholesterol.mg.	111 (77, 163)	53 (1, 84)	0 (0, 0)	<0.001	<0.001
Phosphorus.mg.	637 (598, 892)	667 (428, 817)	541 (390, 776)	0.3	0.4
Magnesium.mg.	163 (129, 185)	156 (140, 214)	239 (164, 328)	0.077	0.2
Zinc.mg.	4.65 (3.83, 6.53)	3.53 (3.00, 5.35)	4.81 (2.92, 7.55)	0.4	0.5
Selenium.μg.	20 (16, 33)	14 (9, 28)	10 (8, 17)	0.021	0.078
Iron.mg.	5.61 (5.12, 7.33)	5.22 (4.35, 6.82)	6.23 (4.76, 8.75)	0.4	0.5
Calcium.mg.	495 (309, 662)	476 (420, 601)	346 (327, 502)	0.2	0.4
Iodine.μg.	38 (24, 54)	38 (22, 58)	21 (14, 39)	0.14	0.3
1 Median (Q1, Q3)
2 Kruskal-Wallis rank sum test
3 False discovery rate correction for multiple testing

food_int_child_old_df <- food_int_child_old$table_body %>%
  as_tibble()

3.1.4 Adults

food_int_adult <- run(
  dat_food %>%
    filter(Age_category == 'Adult') %>% 
    select(-Age_category) %>% 
    tbl_summary(
      by = 'GRP') %>%
    modify_caption("Nutrients intake in adults") %>% 
    add_p() %>% 
    add_q(),
  
  path = 'gitignore/run/food_int_adult', reuse = TRUE)


food_int_adult

Nutrients intake in adults

Characteristic	OM N = 38 1	VG N = 34 1	VN N = 82 1	p-value 2	q-value 3
Calories.kcal.	2,029 (1,698, 2,381)	2,246 (1,701, 2,669)	2,247 (1,844, 2,773)	0.2	0.2
Carbohydrates.g.	214 (178, 248)	221 (199, 289)	284 (228, 337)	<0.001	<0.001
Fat.g.	77 (59, 92)	77 (64, 100)	72 (60, 95)	>0.9	>0.9
Proteins.g.	84 (73, 106)	70 (59, 104)	75 (65, 97)	0.3	0.3
Fiber.g.	20 (16, 26)	32 (22, 42)	38 (30, 52)	<0.001	<0.001
Sugars.g.	62 (46, 88)	63 (44, 102)	65 (50, 81)	0.9	>0.9
Saturated.fats..SAFA..g.	30 (23, 37)	23 (14, 31)	14 (10, 19)	<0.001	<0.001
Cholesterol.mg.	164 (135, 232)	56 (6, 83)	0 (0, 1)	<0.001	<0.001
Phosphorus.mg.	1,186 (987, 1,401)	1,110 (783, 1,399)	1,087 (811, 1,570)	0.5	0.6
Magnesium.mg.	299 (254, 339)	361 (251, 475)	437 (297, 608)	<0.001	<0.001
Zinc.mg.	8.5 (6.5, 10.8)	8.0 (5.0, 12.0)	11.2 (6.6, 14.8)	0.019	0.032
Selenium.μg.	37 (25, 48)	23 (16, 37)	22 (15, 34)	<0.001	<0.001
Iron.mg.	9 (8, 12)	11 (7, 14)	13 (10, 20)	<0.001	<0.001
Calcium.mg.	881 (674, 1,075)	773 (554, 1,058)	711 (555, 952)	0.051	0.076
Iodine.μg.	73 (60, 87)	64 (46, 69)	59 (40, 82)	0.015	0.028
1 Median (Q1, Q3)
2 Kruskal-Wallis rank sum test
3 False discovery rate correction for multiple testing

food_int_adult_df <- food_int_adult$table_body %>%
  as_tibble()

3.2 Plots of nutrient intake

Preparation

## define color
colo <- c('#329243', '#ACE1A3', '#F9FFAF')

## prepare data
df <- dat_food %>% 
  mutate(Age_category2 = recode(Age_category,
                  `Ch > 3 yrs` = 'child_old',
                  `Ch < 3 yrs` = 'child_young')) %>% 
  
  mutate(super_group = interaction(GRP, Age_category2)) %>% 
  
  mutate(super_group = fct_relevel(
    super_group, 
    'OM.child_young', 'VG.child_young', 'VN.child_young',
    'OM.child_old', 'VG.child_old', 'VN.child_old',
    'OM.adult', 'VG.adult', 'VN.adult')) %>% 
  
  mutate(GRP = fct_relevel(
    GRP, 
    'VN', 'VG', 'OM')) %>% 
  
  mutate(Age_category = fct_relevel(
    Age_category, 
    'Ch < 3 yrs', 'Ch > 3 yrs', 'adult'))
    
                              
## define outcomes of interest

outcomes <- data.frame(
  variable = df %>% 
    select(Calories.kcal.:Iodine.μg.) %>% 
    colnames())

outcomes <- outcomes %>% 
  mutate(
    title = str_extract(variable, "^[^.]+"),
    y_title = str_extract(variable, "(?<=\\.).*") %>% 
                     str_replace_all("\\.", "/day")
  )

outcomes$title[1] <- 'Energy'
outcomes$title[7] <- 'Saturate fats'
outcomes$y_title[7] <- 'g/day'

Boxplot

## function to boxplot
boxplot_cond <- function(variable, y_title, title) {
  
  clean_data <- df %>%
    select(Age_category, GRP, super_group,  variable) %>%
    drop_na()
  
  upper_limit <- max(clean_data[[variable]]) * 1.3
  
  p <- ggboxplot(clean_data, 
                 x = 'GRP', 
                 y = variable, 
                 fill = 'GRP', 
                 tip.length = 0.15,
                 palette = colo,
                 outlier.shape = 1,
                 lwd = 0.25,
                 outlier.size = 0.8,
                 facet.by = 'Age_category',
                 title = title,
                 ylab = y_title) +
    
    theme(
      plot.title = element_text(size = 10), 
      axis.title = element_text(size = 8),  
      axis.text = element_text(size = 7),
      axis.title.x = element_blank()
    ) +
  
    coord_cartesian(ylim = c(NA, upper_limit))

  return(p)
}

# Plot all outcomes
plots <- pmap(outcomes, boxplot_cond)

# Create a matrix of plots
plots_arranged <- ggarrange(plotlist = plots, ncol = 4, nrow = 4,  common.legend = TRUE)
plots_arranged

path <- paste0('gitignore/figures/plots_arranged.pdf')

if(file.exists(path) == FALSE){
  ggsave(path, 
         plot = plots_arranged,
         width = 13, height = 10,
         device = cairo_pdf)
}

path <- paste0('gitignore/figures/plots_arranged.tiff')
if(file.exists(path) == FALSE){
  tiff(path, width = 13, height = 10, units = 'in', res = 400) 
  grid.draw(plots_arranged)
  dev.off()
}

4 Reproducibility

sessionInfo()
## R version 4.4.3 (2025-02-28)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=cs_CZ.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=cs_CZ.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=cs_CZ.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=cs_CZ.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Prague
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] mice_3.17.0        patchwork_1.2.0    ggrepel_0.9.5      robustlmm_3.3-1   
##  [5] gridExtra_2.3      pheatmap_1.0.12    performance_0.12.2 quantreg_5.98     
##  [9] SparseM_1.81       bayesplot_1.8.1    ggdist_3.3.2       kableExtra_1.4.0  
## [13] lubridate_1.8.0    corrplot_0.92      arm_1.12-2         MASS_7.3-64       
## [17] projpred_2.0.2     glmnet_4.1-8       boot_1.3-31        cowplot_1.1.1     
## [21] pROC_1.18.0        mgcv_1.9-1         nlme_3.1-167       openxlsx_4.2.5    
## [25] flextable_0.9.6    sjPlot_2.8.16      car_3.1-2          carData_3.0-5     
## [29] gtsummary_2.0.2    emmeans_1.10.4     ggpubr_0.4.0       lme4_1.1-35.5     
## [33] Matrix_1.7-0       forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4       
## [37] purrr_1.0.2        readr_2.1.2        tidyr_1.3.1        tibble_3.2.1      
## [41] ggplot2_3.5.1      tidyverse_1.3.1   
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.4.3           later_1.3.0             gamm4_0.2-6            
##   [4] cellranger_1.1.0        datawizard_0.12.2       rpart_4.1.24           
##   [7] reprex_2.0.1            lifecycle_1.0.4         rstatix_0.7.0          
##  [10] lattice_0.22-5          insight_0.20.2          backports_1.5.0        
##  [13] magrittr_2.0.3          sass_0.4.9              rmarkdown_2.27         
##  [16] yaml_2.3.5              httpuv_1.6.5            zip_2.2.0              
##  [19] askpass_1.1             DBI_1.1.2               minqa_1.2.4            
##  [22] RColorBrewer_1.1-2      multcomp_1.4-18         abind_1.4-5            
##  [25] rvest_1.0.2             nnet_7.3-20             TH.data_1.1-0          
##  [28] sandwich_3.0-1          gdtools_0.3.7           crul_1.5.0             
##  [31] cards_0.2.2             MatrixModels_0.5-3      commonmark_1.9.1       
##  [34] svglite_2.1.3           codetools_0.2-19        xml2_1.3.3             
##  [37] tidyselect_1.2.1        shape_1.4.6             farver_2.1.0           
##  [40] ggeffects_1.7.0         httpcode_0.3.0          base64enc_0.1-3        
##  [43] matrixStats_1.3.0       jsonlite_1.8.8          mitml_0.4-3            
##  [46] ellipsis_0.3.2          ggridges_0.5.3          survival_3.7-0         
##  [49] iterators_1.0.14        systemfonts_1.0.4       foreach_1.5.2          
##  [52] tools_4.4.3             ragg_1.2.1              Rcpp_1.0.13            
##  [55] glue_1.7.0              pan_1.6                 xfun_0.46              
##  [58] distributional_0.4.0    loo_2.4.1               withr_3.0.1            
##  [61] fastmap_1.2.0           fansi_1.0.6             openssl_1.4.6          
##  [64] digest_0.6.37           R6_2.5.1                mime_0.12              
##  [67] estimability_1.5.1      textshaping_0.3.6       colorspace_2.0-2       
##  [70] markdown_1.13           utf8_1.2.4              generics_0.1.3         
##  [73] fontLiberation_0.1.0    data.table_1.15.4       robustbase_0.93-9      
##  [76] httr_1.4.2              htmlwidgets_1.6.4       pkgconfig_2.0.3        
##  [79] gtable_0.3.0            htmltools_0.5.8.1       fontBitstreamVera_0.1.1
##  [82] scales_1.3.0            knitr_1.48              rstudioapi_0.16.0      
##  [85] tzdb_0.2.0              uuid_1.0-3              coda_0.19-4            
##  [88] curl_4.3.2              nloptr_2.0.0            zoo_1.8-9              
##  [91] sjlabelled_1.2.0        parallel_4.4.3          pillar_1.9.0           
##  [94] vctrs_0.6.5             promises_1.2.0.1        jomo_2.7-3             
##  [97] dbplyr_2.1.1            xtable_1.8-4            evaluate_1.0.0         
## [100] fastGHQuad_1.0.1        mvtnorm_1.1-3           cli_3.6.3              
## [103] compiler_4.4.3          rlang_1.1.4             crayon_1.5.0           
## [106] rstantools_2.1.1        ggsignif_0.6.3          labeling_0.4.2         
## [109] modelr_0.1.8            plyr_1.8.6              sjmisc_2.8.10          
## [112] fs_1.6.4                stringi_1.7.6           viridisLite_0.4.0      
## [115] assertthat_0.2.1        munsell_0.5.0           fontquiver_0.2.1       
## [118] sjstats_0.19.0          hms_1.1.1               gfonts_0.2.0           
## [121] shiny_1.9.1             haven_2.4.3             gt_0.11.0              
## [124] broom_1.0.6             DEoptimR_1.0-10         readxl_1.3.1           
## [127] officer_0.6.6